how do speech recognition systems work

How Does Speech Recognition Work? (9 Simple Questions Answered)

• by Team Experts
• July 2, 2023 July 3, 2023

Discover the Surprising Science Behind Speech Recognition – Learn How It Works in 9 Simple Questions!

Speech recognition is the process of converting spoken words into written or machine-readable text. It is achieved through a combination of natural language processing , audio inputs, machine learning , and voice recognition . Speech recognition systems analyze speech patterns to identify phonemes , the basic units of sound in a language. Acoustic modeling is used to match the phonemes to words , and word prediction algorithms are used to determine the most likely words based on context analysis . Finally, the words are converted into text.

What is Natural Language Processing and How Does it Relate to Speech Recognition?

How do audio inputs enable speech recognition, what role does machine learning play in speech recognition, how does voice recognition work, what are the different types of speech patterns used for speech recognition, how is acoustic modeling used for accurate phoneme detection in speech recognition systems, what is word prediction and why is it important for effective speech recognition technology, how can context analysis improve accuracy of automatic speech recognition systems, common mistakes and misconceptions.

Natural language processing (NLP) is a branch of artificial intelligence that deals with the analysis and understanding of human language. It is used to enable machines to interpret and process natural language, such as speech, text, and other forms of communication . NLP is used in a variety of applications , including automated speech recognition , voice recognition technology , language models, text analysis , text-to-speech synthesis , natural language understanding , natural language generation, semantic analysis , syntactic analysis, pragmatic analysis, sentiment analysis, and speech-to-text conversion. NLP is closely related to speech recognition , as it is used to interpret and understand spoken language in order to convert it into text.

Audio inputs enable speech recognition by providing digital audio recordings of spoken words . These recordings are then analyzed to extract acoustic features of speech, such as pitch, frequency, and amplitude. Feature extraction techniques , such as spectral analysis of sound waves, are used to identify and classify phonemes . Natural language processing (NLP) and machine learning models are then used to interpret the audio recordings and recognize speech. Neural networks and deep learning architectures are used to further improve the accuracy of voice recognition . Finally, Automatic Speech Recognition (ASR) systems are used to convert the speech into text, and noise reduction techniques and voice biometrics are used to improve accuracy .

Machine learning plays a key role in speech recognition , as it is used to develop algorithms that can interpret and understand spoken language. Natural language processing , pattern recognition techniques , artificial intelligence , neural networks, acoustic modeling , language models, statistical methods , feature extraction , hidden Markov models (HMMs), deep learning architectures , voice recognition systems, speech synthesis , and automatic speech recognition (ASR) are all used to create machine learning models that can accurately interpret and understand spoken language. Natural language understanding is also used to further refine the accuracy of the machine learning models .

Voice recognition works by using machine learning algorithms to analyze the acoustic properties of a person’s voice. This includes using voice recognition software to identify phonemes , speaker identification, text normalization , language models, noise cancellation techniques , prosody analysis , contextual understanding , artificial neural networks, voice biometrics , speech synthesis , and deep learning . The data collected is then used to create a voice profile that can be used to identify the speaker .

The different types of speech patterns used for speech recognition include prosody , contextual speech recognition , speaker adaptation , language models, hidden Markov models (HMMs), neural networks, Gaussian mixture models (GMMs) , discrete wavelet transform (DWT), Mel-frequency cepstral coefficients (MFCCs), vector quantization (VQ), dynamic time warping (DTW), continuous density hidden Markov model (CDHMM), support vector machines (SVM), and deep learning .

Acoustic modeling is used for accurate phoneme detection in speech recognition systems by utilizing statistical models such as Hidden Markov Models (HMMs) and Gaussian Mixture Models (GMMs). Feature extraction techniques such as Mel-frequency cepstral coefficients (MFCCs) are used to extract relevant features from the audio signal . Context-dependent models are also used to improve accuracy . Discriminative training techniques such as maximum likelihood estimation and the Viterbi algorithm are used to train the models. In recent years, neural networks and deep learning algorithms have been used to improve accuracy , as well as natural language processing techniques .

Word prediction is a feature of natural language processing and artificial intelligence that uses machine learning algorithms to predict the next word or phrase a user is likely to type or say. It is used in automated speech recognition systems to improve the accuracy of the system by reducing the amount of user effort and time spent typing or speaking words. Word prediction also enhances the user experience by providing faster response times and increased efficiency in data entry tasks. Additionally, it reduces errors due to incorrect spelling or grammar, and improves the understanding of natural language by machines. By using word prediction, speech recognition technology can be more effective , providing improved accuracy and enhanced ability for machines to interpret human speech.

Context analysis can improve the accuracy of automatic speech recognition systems by utilizing language models, acoustic models, statistical methods , and machine learning algorithms to analyze the semantic , syntactic, and pragmatic aspects of speech. This analysis can include word – level , sentence- level , and discourse-level context, as well as utterance understanding and ambiguity resolution. By taking into account the context of the speech, the accuracy of the automatic speech recognition system can be improved.

• Misconception : Speech recognition requires a person to speak in a robotic , monotone voice. Correct Viewpoint: Speech recognition technology is designed to recognize natural speech patterns and does not require users to speak in any particular way.
• Misconception : Speech recognition can understand all languages equally well. Correct Viewpoint: Different speech recognition systems are designed for different languages and dialects, so the accuracy of the system will vary depending on which language it is programmed for.
• Misconception: Speech recognition only works with pre-programmed commands or phrases . Correct Viewpoint: Modern speech recognition systems are capable of understanding conversational language as well as specific commands or phrases that have been programmed into them by developers.

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Speech recognition software

by Chris Woodford . Last updated: August 17, 2023.

I t's just as well people can understand speech. Imagine if you were like a computer: friends would have to "talk" to you by prodding away at a plastic keyboard connected to your brain by a long, curly wire. If you wanted to say "hello" to someone, you'd have to reach out, chatter your fingers over their keyboard, and wait for their eyes to light up; they'd have to do the same to you. Conversations would be a long, slow, elaborate nightmare—a silent dance of fingers on plastic; strange, abstract, and remote. We'd never put up with such clumsiness as humans, so why do we talk to our computers this way?

Scientists have long dreamed of building machines that can chatter and listen just like humans. But although computerized speech recognition has been around for decades, and is now built into most smartphones and PCs, few of us actually use it. Why? Possibly because we never even bother to try it out, working on the assumption that computers could never pull off a trick so complex as understanding the human voice. It's certainly true that speech recognition is a complex problem that's challenged some of the world's best computer scientists, mathematicians, and linguists. How well are they doing at cracking the problem? Will we all be chatting to our PCs one day soon? Let's take a closer look and find out!

Photo: A court reporter dictates notes into a laptop with a noise-cancelling microphone and speech-recogition software. Photo by Micha Pierce courtesy of US Marine Corps and DVIDS .

What is speech?

Language sets people far above our creeping, crawling animal friends. While the more intelligent creatures, such as dogs and dolphins, certainly know how to communicate with sounds, only humans enjoy the rich complexity of language. With just a couple of dozen letters, we can build any number of words (most dictionaries contain tens of thousands) and express an infinite number of thoughts.

Photo: Speech recognition has been popping up all over the place for quite a few years now. Even my old iPod Touch (dating from around 2012) has a built-in "voice control" program that let you pick out music just by saying "Play albums by U2," or whatever band you're in the mood for.

When we speak, our voices generate little sound packets called phones (which correspond to the sounds of letters or groups of letters in words); so speaking the word cat produces phones that correspond to the sounds "c," "a," and "t." Although you've probably never heard of these kinds of phones before, you might well be familiar with the related concept of phonemes : simply speaking, phonemes are the basic LEGO™ blocks of sound that all words are built from. Although the difference between phones and phonemes is complex and can be very confusing, this is one "quick-and-dirty" way to remember it: phones are actual bits of sound that we speak (real, concrete things), whereas phonemes are ideal bits of sound we store (in some sense) in our minds (abstract, theoretical sound fragments that are never actually spoken).

Computers and computer models can juggle around with phonemes, but the real bits of speech they analyze always involves processing phones. When we listen to speech, our ears catch phones flying through the air and our leaping brains flip them back into words, sentences, thoughts, and ideas—so quickly, that we often know what people are going to say before the words have fully fled from their mouths. Instant, easy, and quite dazzling, our amazing brains make this seem like a magic trick. And it's perhaps because listening seems so easy to us that we think computers (in many ways even more amazing than brains) should be able to hear, recognize, and decode spoken words as well. If only it were that simple!

Why is speech so hard to handle?

The trouble is, listening is much harder than it looks (or sounds): there are all sorts of different problems going on at the same time... When someone speaks to you in the street, there's the sheer difficulty of separating their words (what scientists would call the acoustic signal ) from the background noise —especially in something like a cocktail party, where the "noise" is similar speech from other conversations. When people talk quickly, and run all their words together in a long stream, how do we know exactly when one word ends and the next one begins? (Did they just say "dancing and smile" or "dance, sing, and smile"?) There's the problem of how everyone's voice is a little bit different, and the way our voices change from moment to moment. How do our brains figure out that a word like "bird" means exactly the same thing when it's trilled by a ten year-old girl or boomed by her forty-year-old father? What about words like "red" and "read" that sound identical but mean totally different things (homophones, as they're called)? How does our brain know which word the speaker means? What about sentences that are misheard to mean radically different things? There's the age-old military example of "send reinforcements, we're going to advance" being misheard for "send three and fourpence, we're going to a dance"—and all of us can probably think of song lyrics we've hilariously misunderstood the same way (I always chuckle when I hear Kate Bush singing about "the cattle burning over your shoulder"). On top of all that stuff, there are issues like syntax (the grammatical structure of language) and semantics (the meaning of words) and how they help our brain decode the words we hear, as we hear them. Weighing up all these factors, it's easy to see that recognizing and understanding spoken words in real time (as people speak to us) is an astonishing demonstration of blistering brainpower.

It shouldn't surprise or disappoint us that computers struggle to pull off the same dazzling tricks as our brains; it's quite amazing that they get anywhere near!

Photo: Using a headset microphone like this makes a huge difference to the accuracy of speech recognition: it reduces background sound, making it much easier for the computer to separate the signal (the all-important words you're speaking) from the noise (everything else).

How do computers recognize speech?

Speech recognition is one of the most complex areas of computer science —and partly because it's interdisciplinary: it involves a mixture of extremely complex linguistics, mathematics, and computing itself. If you read through some of the technical and scientific papers that have been published in this area (a few are listed in the references below), you may well struggle to make sense of the complexity. My objective is to give a rough flavor of how computers recognize speech, so—without any apology whatsoever—I'm going to simplify hugely and miss out most of the details.

Broadly speaking, there are four different approaches a computer can take if it wants to turn spoken sounds into written words:

1: Simple pattern matching

Ironically, the simplest kind of speech recognition isn't really anything of the sort. You'll have encountered it if you've ever phoned an automated call center and been answered by a computerized switchboard. Utility companies often have systems like this that you can use to leave meter readings, and banks sometimes use them to automate basic services like balance inquiries, statement orders, checkbook requests, and so on. You simply dial a number, wait for a recorded voice to answer, then either key in or speak your account number before pressing more keys (or speaking again) to select what you want to do. Crucially, all you ever get to do is choose one option from a very short list, so the computer at the other end never has to do anything as complex as parsing a sentence (splitting a string of spoken sound into separate words and figuring out their structure), much less trying to understand it; it needs no knowledge of syntax (language structure) or semantics (meaning). In other words, systems like this aren't really recognizing speech at all: they simply have to be able to distinguish between ten different sound patterns (the spoken words zero through nine) either using the bleeping sounds of a Touch-Tone phone keypad (technically called DTMF ) or the spoken sounds of your voice.

From a computational point of view, there's not a huge difference between recognizing phone tones and spoken numbers "zero", "one," "two," and so on: in each case, the system could solve the problem by comparing an entire chunk of sound to similar stored patterns in its memory. It's true that there can be quite a bit of variability in how different people say "three" or "four" (they'll speak in a different tone, more or less slowly, with different amounts of background noise) but the ten numbers are sufficiently different from one another for this not to present a huge computational challenge. And if the system can't figure out what you're saying, it's easy enough for the call to be transferred automatically to a human operator.

Photo: Voice-activated dialing on cellphones is little more than simple pattern matching. You simply train the phone to recognize the spoken version of a name in your phonebook. When you say a name, the phone doesn't do any particularly sophisticated analysis; it simply compares the sound pattern with ones you've stored previously and picks the best match. No big deal—which explains why even an old phone like this 2001 Motorola could do it.

2: Pattern and feature analysis

Automated switchboard systems generally work very reliably because they have such tiny vocabularies: usually, just ten words representing the ten basic digits. The vocabulary that a speech system works with is sometimes called its domain . Early speech systems were often optimized to work within very specific domains, such as transcribing doctor's notes, computer programming commands, or legal jargon, which made the speech recognition problem far simpler (because the vocabulary was smaller and technical terms were explicitly trained beforehand). Much like humans, modern speech recognition programs are so good that they work in any domain and can recognize tens of thousands of different words. How do they do it?

Most of us have relatively large vocabularies, made from hundreds of common words ("a," "the," "but" and so on, which we hear many times each day) and thousands of less common ones (like "discombobulate," "crepuscular," "balderdash," or whatever, which we might not hear from one year to the next). Theoretically, you could train a speech recognition system to understand any number of different words, just like an automated switchboard: all you'd need to do would be to get your speaker to read each word three or four times into a microphone, until the computer generalized the sound pattern into something it could recognize reliably.

The trouble with this approach is that it's hugely inefficient. Why learn to recognize every word in the dictionary when all those words are built from the same basic set of sounds? No-one wants to buy an off-the-shelf computer dictation system only to find they have to read three or four times through a dictionary, training it up to recognize every possible word they might ever speak, before they can do anything useful. So what's the alternative? How do humans do it? We don't need to have seen every Ford, Chevrolet, and Cadillac ever manufactured to recognize that an unknown, four-wheeled vehicle is a car: having seen many examples of cars throughout our lives, our brains somehow store what's called a prototype (the generalized concept of a car, something with four wheels, big enough to carry two to four passengers, that creeps down a road) and we figure out that an object we've never seen before is a car by comparing it with the prototype. In much the same way, we don't need to have heard every person on Earth read every word in the dictionary before we can understand what they're saying; somehow we can recognize words by analyzing the key features (or components) of the sounds we hear. Speech recognition systems take the same approach.

The recognition process

Practical speech recognition systems start by listening to a chunk of sound (technically called an utterance ) read through a microphone. The first step involves digitizing the sound (so the up-and-down, analog wiggle of the sound waves is turned into digital format, a string of numbers) by a piece of hardware (or software) called an analog-to-digital (A/D) converter (for a basic introduction, see our article on analog versus digital technology ). The digital data is converted into a spectrogram (a graph showing how the component frequencies of the sound change in intensity over time) using a mathematical technique called a Fast Fourier Transform (FFT) ), then broken into a series of overlapping chunks called acoustic frames , each one typically lasting 1/25 to 1/50 of a second. These are digitally processed in various ways and analyzed to find the components of speech they contain. Assuming we've separated the utterance into words, and identified the key features of each one, all we have to do is compare what we have with a phonetic dictionary (a list of known words and the sound fragments or features from which they're made) and we can identify what's probably been said. Probably is always the word in speech recognition: no-one but the speaker can ever know exactly what was said.)

Seeing speech

In theory, since spoken languages are built from only a few dozen phonemes (English uses about 46, while Spanish has only about 24), you could recognize any possible spoken utterance just by learning to pick out phones (or similar key features of spoken language such as formants , which are prominent frequencies that can be used to help identify vowels). Instead of having to recognize the sounds of (maybe) 40,000 words, you'd only need to recognize the 46 basic component sounds (or however many there are in your language), though you'd still need a large phonetic dictionary listing the phonemes that make up each word. This method of analyzing spoken words by identifying phones or phonemes is often called the beads-on-a-string model : a chunk of unknown speech (the string) is recognized by breaking it into phones or bits of phones (the beads); figure out the phones and you can figure out the words.

Most speech recognition programs get better as you use them because they learn as they go along using feedback you give them, either deliberately (by correcting mistakes) or by default (if you don't correct any mistakes, you're effectively saying everything was recognized perfectly—which is also feedback). If you've ever used a program like one of the Dragon dictation systems, you'll be familiar with the way you have to correct your errors straight away to ensure the program continues to work with high accuracy. If you don't correct mistakes, the program assumes it's recognized everything correctly, which means similar mistakes are even more likely to happen next time. If you force the system to go back and tell it which words it should have chosen, it will associate those corrected words with the sounds it heard—and do much better next time.

Screenshot: With speech dictation programs like Dragon NaturallySpeaking, shown here, it's important to go back and correct your mistakes if you want your words to be recognized accurately in future.

3: Statistical analysis

In practice, recognizing speech is much more complex than simply identifying phones and comparing them to stored patterns, and for a whole variety of reasons: Speech is extremely variable: different people speak in different ways (even though we're all saying the same words and, theoretically, they're all built from a standard set of phonemes) You don't always pronounce a certain word in exactly the same way; even if you did, the way you spoke a word (or even part of a word) might vary depending on the sounds or words that came before or after. As a speaker's vocabulary grows, the number of similar-sounding words grows too: the digits zero through nine all sound different when you speak them, but "zero" sounds like "hero," "one" sounds like "none," "two" could mean "two," "to," or "too"... and so on. So recognizing numbers is a tougher job for voice dictation on a PC, with a general 50,000-word vocabulary, than for an automated switchboard with a very specific, 10-word vocabulary containing only the ten digits. The more speakers a system has to recognize, the more variability it's going to encounter and the bigger the likelihood of making mistakes. For something like an off-the-shelf voice dictation program (one that listens to your voice and types your words on the screen), simple pattern recognition is clearly going to be a bit hit and miss. The basic principle of recognizing speech by identifying its component parts certainly holds good, but we can do an even better job of it by taking into account how language really works. In other words, we need to use what's called a language model .

When people speak, they're not simply muttering a series of random sounds. Every word you utter depends on the words that come before or after. For example, unless you're a contrary kind of poet, the word "example" is much more likely to follow words like "for," "an," "better," "good", "bad," and so on than words like "octopus," "table," or even the word "example" itself. Rules of grammar make it unlikely that a noun like "table" will be spoken before another noun ("table example" isn't something we say) while—in English at least—adjectives ("red," "good," "clear") come before nouns and not after them ("good example" is far more probable than "example good"). If a computer is trying to figure out some spoken text and gets as far as hearing "here is a ******* example," it can be reasonably confident that ******* is an adjective and not a noun. So it can use the rules of grammar to exclude nouns like "table" and the probability of pairs like "good example" and "bad example" to make an intelligent guess. If it's already identified a "g" sound instead of a "b", that's an added clue.

Virtually all modern speech recognition systems also use a bit of complex statistical hocus-pocus to help figure out what's being said. The probability of one phone following another, the probability of bits of silence occurring in between phones, and the likelihood of different words following other words are all factored in. Ultimately, the system builds what's called a hidden Markov model (HMM) of each speech segment, which is the computer's best guess at which beads are sitting on the string, based on all the things it's managed to glean from the sound spectrum and all the bits and pieces of phones and silence that it might reasonably contain. It's called a Markov model (or Markov chain), for Russian mathematician Andrey Markov , because it's a sequence of different things (bits of phones, words, or whatever) that change from one to the next with a certain probability. Confusingly, it's referred to as a "hidden" Markov model even though it's worked out in great detail and anything but hidden! "Hidden," in this case, simply means the contents of the model aren't observed directly but figured out indirectly from the sound spectrum. From the computer's viewpoint, speech recognition is always a probabilistic "best guess" and the right answer can never be known until the speaker either accepts or corrects the words that have been recognized. (Markov models can be processed with an extra bit of computer jiggery pokery called the Viterbi algorithm , but that's beyond the scope of this article.)

4: Artificial neural networks

HMMs have dominated speech recognition since the 1970s—for the simple reason that they work so well. But they're by no means the only technique we can use for recognizing speech. There's no reason to believe that the brain itself uses anything like a hidden Markov model. It's much more likely that we figure out what's being said using dense layers of brain cells that excite and suppress one another in intricate, interlinked ways according to the input signals they receive from our cochleas (the parts of our inner ear that recognize different sound frequencies).

Back in the 1980s, computer scientists developed "connectionist" computer models that could mimic how the brain learns to recognize patterns, which became known as artificial neural networks (sometimes called ANNs). A few speech recognition scientists explored using neural networks, but the dominance and effectiveness of HMMs relegated alternative approaches like this to the sidelines. More recently, scientists have explored using ANNs and HMMs side by side and found they give significantly higher accuracy over HMMs used alone.

Artwork: Neural networks are hugely simplified, computerized versions of the brain—or a tiny part of it that have inputs (where you feed in information), outputs (where results appear), and hidden units (connecting the two). If you train them with enough examples, they learn by gradually adjusting the strength of the connections between the different layers of units. Once a neural network is fully trained, if you show it an unknown example, it will attempt to recognize what it is based on the examples it's seen before.

Speech recognition: a summary

Artwork: A summary of some of the key stages of speech recognition and the computational processes happening behind the scenes.

What can we use speech recognition for?

We've already touched on a few of the more common applications of speech recognition, including automated telephone switchboards and computerized voice dictation systems. But there are plenty more examples where those came from.

Many of us (whether we know it or not) have cellphones with voice recognition built into them. Back in the late 1990s, state-of-the-art mobile phones offered voice-activated dialing , where, in effect, you recorded a sound snippet for each entry in your phonebook, such as the spoken word "Home," or whatever that the phone could then recognize when you spoke it in future. A few years later, systems like SpinVox became popular helping mobile phone users make sense of voice messages by converting them automatically into text (although a sneaky BBC investigation eventually claimed that some of its state-of-the-art speech automated speech recognition was actually being done by humans in developing countries!).

Today's smartphones make speech recognition even more of a feature. Apple's Siri , Google Assistant ("Hey Google..."), and Microsoft's Cortana are smartphone "personal assistant apps" who'll listen to what you say, figure out what you mean, then attempt to do what you ask, whether it's looking up a phone number or booking a table at a local restaurant. They work by linking speech recognition to complex natural language processing (NLP) systems, so they can figure out not just what you say , but what you actually mean , and what you really want to happen as a consequence. Pressed for time and hurtling down the street, mobile users theoretically find this kind of system a boon—at least if you believe the hype in the TV advertisements that Google and Microsoft have been running to promote their systems. (Google quietly incorporated speech recognition into its search engine some time ago, so you can Google just by talking to your smartphone, if you really want to.) If you have one of the latest voice-powered electronic assistants, such as Amazon's Echo/Alexa or Google Home, you don't need a computer of any kind (desktop, tablet, or smartphone): you just ask questions or give simple commands in your natural language to a thing that resembles a loudspeaker ... and it answers straight back.

Screenshot: When I asked Google "does speech recognition really work," it took it three attempts to recognize the question correctly.

Will speech recognition ever take off?

I'm a huge fan of speech recognition. After suffering with repetitive strain injury on and off for some time, I've been using computer dictation to write quite a lot of my stuff for about 15 years, and it's been amazing to see the improvements in off-the-shelf voice dictation over that time. The early Dragon NaturallySpeaking system I used on a Windows 95 laptop was fairly reliable, but I had to speak relatively slowly, pausing slightly between each word or word group, giving a horribly staccato style that tended to interrupt my train of thought. This slow, tedious one-word-at-a-time approach ("can – you – tell – what – I – am – saying – to – you") went by the name discrete speech recognition . A few years later, things had improved so much that virtually all the off-the-shelf programs like Dragon were offering continuous speech recognition , which meant I could speak at normal speed, in a normal way, and still be assured of very accurate word recognition. When you can speak normally to your computer, at a normal talking pace, voice dictation programs offer another advantage: they give clumsy, self-conscious writers a much more attractive, conversational style: "write like you speak" (always a good tip for writers) is easy to put into practice when you speak all your words as you write them!

Despite the technological advances, I still generally prefer to write with a keyboard and mouse . Ironically, I'm writing this article that way now. Why? Partly because it's what I'm used to. I often write highly technical stuff with a complex vocabulary that I know will defeat the best efforts of all those hidden Markov models and neural networks battling away inside my PC. It's easier to type "hidden Markov model" than to mutter those words somewhat hesitantly, watch "hiccup half a puddle" pop up on screen and then have to make corrections.

Screenshot: You an always add more words to a speech recognition program. Here, I've decided to train the Microsoft Windows built-in speech recognition engine to spot the words 'hidden Markov model.'

Mobile revolution?

You might think mobile devices—with their slippery touchscreens —would benefit enormously from speech recognition: no-one really wants to type an essay with two thumbs on a pop-up QWERTY keyboard. Ironically, mobile devices are heavily used by younger, tech-savvy kids who still prefer typing and pawing at screens to speaking out loud. Why? All sorts of reasons, from sheer familiarity (it's quick to type once you're used to it—and faster than fixing a computer's goofed-up guesses) to privacy and consideration for others (many of us use our mobile phones in public places and we don't want our thoughts wide open to scrutiny or howls of derision), and the sheer difficulty of speaking clearly and being clearly understood in noisy environments. Recently, I was walking down a street and overheard a small garden party where the sounds of happy laughter, drinking, and discreet background music were punctuated by a sudden grunt of "Alexa play Copacabana by Barry Manilow"—which silenced the conversation entirely and seemed jarringly out of place. Speech recognition has never been so indiscreet. What you're doing with your computer also makes a difference. If you've ever used speech recognition on a PC, you'll know that writing something like an essay (dictating hundreds or thousands of words of ordinary text) is a whole lot easier than editing it afterwards (where you laboriously try to select words or sentences and move them up or down so many lines with awkward cut and paste commands). And trying to open and close windows, start programs, or navigate around a computer screen by voice alone is clumsy, tedious, error-prone, and slow. It's far easier just to click your mouse or swipe your finger.

Photo: Here I'm using Google's Live Transcribe app to dictate the last paragraph of this article. As you can see, apart from the punctuation, the transcription is flawless, without any training at all. This is the fastest and most accurate speech recognition software I've ever used. It's mainly designed as an accessibility aid for deaf and hard of hearing people, but it can be used for dictation too.

Developers of speech recognition systems insist everything's about to change, largely thanks to natural language processing and smart search engines that can understand spoken queries. ("OK Google...") But people have been saying that for decades now: the brave new world is always just around the corner. According to speech pioneer James Baker, better speech recognition "would greatly increase the speed and ease with which humans could communicate with computers, and greatly speed and ease the ability with which humans could record and organize their own words and thoughts"—but he wrote (or perhaps voice dictated?) those words 25 years ago! Just because Google can now understand speech, it doesn't follow that we automatically want to speak our queries rather than type them—especially when you consider some of the wacky things people look for online. Humans didn't invent written language because others struggled to hear and understand what they were saying. Writing and speaking serve different purposes. Writing is a way to set out longer, more clearly expressed and elaborated thoughts without having to worry about the limitations of your short-term memory; speaking is much more off-the-cuff. Writing is grammatical; speech doesn't always play by the rules. Writing is introverted, intimate, and inherently private; it's carefully and thoughtfully composed. Speaking is an altogether different way of expressing your thoughts—and people don't always want to speak their minds. While technology may be ever advancing, it's far from certain that speech recognition will ever take off in quite the way that its developers would like. I'm typing these words, after all, not speaking them.

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• Microphones
• Neural networks
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• Automatic Speech Recognition: A Deep Learning Approach by Dong Yu and Li Deng. Springer, 2015. Two Microsoft researchers review state-of-the-art, neural-network approaches to recognition.
• Theory and Applications of Digital Speech Processing by Lawrence R. Rabiner and Ronald W. Schafer. Pearson, 2011. An up-to-date review at undergraduate level.
• Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition by Daniel Jurafsky, James Martin. Prentice Hall, 2009. An up-to-date, interdisciplinary review of speech recognition technology.
• Statistical Methods for Speech Recognition by Frederick Jelinek. MIT Press, 1997. A detailed guide to Hidden Markov Models and the other statistical techniques that computers use to figure out human speech.
• Fundamentals of Speech Recognition by Lawrence R. Rabiner and Biing-Hwang Juang. PTR Prentice Hall, 1993. A little dated now, but still a good introduction to the basic concepts.
• Speech Recognition: Invited Papers Presented at the 1974 IEEE Symposium by D. R. Reddy (ed). Academic Press, 1975. A classic collection of pioneering papers from the golden age of the 1970s.

Easy-to-understand

• Lost voices, ignored words: Apple's speech recognition needs urgent reform by Colin Hughes, The Register, 16 August 2023. How speech recognition software ignores the needs of the people who need it most—disabled people with different accessibility needs.
• Android's Live Transcribe will let you save transcriptions and show 'sound events' by Dieter Bohn, The Verge, 16 May 2019. An introduction to Google's handy, 70-language transcription app.
• Hey, Siri: Read My Lips by Emily Waltz, IEEE Spectrum, 8 February 2019. How your computer can translate your words... without even listening.
• Interpol's New Software Will Recognize Criminals by Their Voices by Michael Dumiak, 16 May 2018. Is it acceptable for law enforcement agencies to store huge quantities of our voice samples if it helps them trap the occasional bad guy?
• Cypher: The Deep-Learning Software That Will Help Siri, Alexa, and Cortana Hear You : by Amy Nordrum. IEEE Spectrum, 24 October 2016. Cypher helps voice recognition programs to separate speech signals from background noise.
• In the Future, How Will We Talk to Our Technology? : by David Pierce. Wired, 27 September 2015. What sort of hardware will we use with future speech recognition software?
• The Holy Grail of Speech Recognition by Janie Chang: Microsoft Research, 29 August 2011. How neural networks are making a comeback in speech recognition research. [Archived via the Wayback Machine.]
• Audio Alchemy: Getting Computers to Understand Overlapping Speech by John R. Hershey et al. Scientific American, April 12, 2011. How can computers make sense of two people talking at once?
• How Siri Works: Interview with Tom Gruber by Nova Spivack, Minding the Planet, 26 January 2010. Gruber explains some of the technical tricks that allow Siri to understand natural language.
• A sound start for speech tech : by LJ Rich. BBC News, 15 May 2009. Cambridge University's Dr Tony Robinson talks us through the science of speech recognition.
• Speech Recognition by Computer by Stephen E. Levinson and Mark Y. Liberman, Scientific American, Vol. 244, No. 4 (April 1981), pp. 64–77. A more detailed overview of the basic concepts. A good article to continue with after you've read mine.

More technical

• An All-Neural On-Device Speech Recognizer by Johan Schalkwyk, Google AI Blog, March 12, 2019. Google announces a state-of-the-art speech recognition system based entirely on what are called recurrent neural network transducers (RNN-Ts).
• Improving End-to-End Models For Speech Recognition by Tara N. Sainath, and Yonghui Wu, Google Research Blog, December 14, 2017. A cutting-edge speech recognition model that integrates traditionally separate aspects of speech recognition into a single system.
• A Historical Perspective of Speech Recognition by Xuedong Huang, James Baker, Raj Reddy. Communications of the ACM, January 2014 (Vol. 57 No. 1), Pages 94–103.
• [PDF] Application Of Pretrained Deep Neural Networks To Large Vocabulary Speech Recognition by Navdeep Jaitly, Patrick Nguyen, Andrew Senior, Vincent Vanhoucke. Proceedings of Interspeech 2012. An insight into Google's use of neural networks for speech recognition.
• Context-Dependent Pre-trained Deep Neural Networks for Large Vocabulary Speech Recognition by George Dahl et al. IEEE Transactions on Audio, Speech, and Language Processing, Vol. 20 No. 1, January 2012. A review of Microsoft's recent research into using neural networks with HMMs.
• Speech Recognition Technology: A Critique by Stephen E. Levinson, Proceedings of the National Academy of Sciences of the United States of America. Vol. 92, No. 22, October 24, 1995, pp. 9953–9955.
• Hidden Markov Models for Speech Recognition by B. H. Juang and L. R. Rabiner, Technometrics, Vol. 33, No. 3, August, 1991, pp. 251–272.
• A Tutorial on Hidden Markov Models and Selected Applications in Speech Recognition by Lawrence R. Rabiner. Proceedings of the IEEE, Vol 77 No 2, February 1989. A classic introduction to Markov models, though non-mathematicians will find it tough going.
• US Patent: 4,783,803: Speech recognition apparatus and method by James K. Baker, Dragon Systems, 8 November 1988. One of Baker's first Dragon patents. Another Baker patent filed the following year follows on from this. See US Patent: 4,866,778: Interactive speech recognition apparatus by James K. Baker, Dragon Systems, 12 September 1989.
• US Patent 4,783,804: Hidden Markov model speech recognition arrangement by Stephen E. Levinson, Lawrence R. Rabiner, and Man M. Sondi, AT&T Bell Laboratories, 6 May 1986. Sets out one approach to probabilistic speech recognition using Markov models.
• US Patent: 4,363,102: Speaker identification system using word recognition templates by John E. Holmgren, Bell Labs, 7 December 1982. A method of recognizing a particular person's voice using analysis of key features.
• US Patent 2,938,079: Spectrum segmentation system for the automatic extraction of formant frequencies from human speech by James L. Flanagan, US Air Force, 24 May 1960. An early speech recognition system based on formant (peak frequency) analysis.
• A Historical Perspective of Speech Recognition by Raj Reddy (an AI researcher at Carnegie Mellon), James Baker (founder of Dragon), and Xuedong Huang (of Microsoft). Speech recognition pioneers look back on the advances they helped to inspire in this four-minute discussion.

Text copyright © Chris Woodford 2007, 2020. All rights reserved. Full copyright notice and terms of use .

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Speech recognition, also known as automatic speech recognition (ASR), computer speech recognition or speech-to-text, is a capability that enables a program to process human speech into a written format.

While speech recognition is commonly confused with voice recognition, speech recognition focuses on the translation of speech from a verbal format to a text one whereas voice recognition just seeks to identify an individual user’s voice.

IBM has had a prominent role within speech recognition since its inception, releasing of “Shoebox” in 1962. This machine had the ability to recognize 16 different words, advancing the initial work from Bell Labs from the 1950s. However, IBM didn’t stop there, but continued to innovate over the years, launching VoiceType Simply Speaking application in 1996. This speech recognition software had a 42,000-word vocabulary, supported English and Spanish, and included a spelling dictionary of 100,000 words.

While speech technology had a limited vocabulary in the early days, it is utilized in a wide number of industries today, such as automotive, technology, and healthcare. Its adoption has only continued to accelerate in recent years due to advancements in deep learning and big data.  Research  (link resides outside ibm.com) shows that this market is expected to be worth USD 24.9 billion by 2025.

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Many speech recognition applications and devices are available, but the more advanced solutions use AI and machine learning . They integrate grammar, syntax, structure, and composition of audio and voice signals to understand and process human speech. Ideally, they learn as they go — evolving responses with each interaction.

The best kind of systems also allow organizations to customize and adapt the technology to their specific requirements — everything from language and nuances of speech to brand recognition. For example:

• Language weighting: Improve precision by weighting specific words that are spoken frequently (such as product names or industry jargon), beyond terms already in the base vocabulary.
• Speaker labeling: Output a transcription that cites or tags each speaker’s contributions to a multi-participant conversation.
• Acoustics training: Attend to the acoustical side of the business. Train the system to adapt to an acoustic environment (like the ambient noise in a call center) and speaker styles (like voice pitch, volume and pace).
• Profanity filtering: Use filters to identify certain words or phrases and sanitize speech output.

Meanwhile, speech recognition continues to advance. Companies, like IBM, are making inroads in several areas, the better to improve human and machine interaction.

The vagaries of human speech have made development challenging. It’s considered to be one of the most complex areas of computer science – involving linguistics, mathematics and statistics. Speech recognizers are made up of a few components, such as the speech input, feature extraction, feature vectors, a decoder, and a word output. The decoder leverages acoustic models, a pronunciation dictionary, and language models to determine the appropriate output.

Speech recognition technology is evaluated on its accuracy rate, i.e. word error rate (WER), and speed. A number of factors can impact word error rate, such as pronunciation, accent, pitch, volume, and background noise. Reaching human parity – meaning an error rate on par with that of two humans speaking – has long been the goal of speech recognition systems. Research from Lippmann (link resides outside ibm.com) estimates the word error rate to be around 4 percent, but it’s been difficult to replicate the results from this paper.

Various algorithms and computation techniques are used to recognize speech into text and improve the accuracy of transcription. Below are brief explanations of some of the most commonly used methods:

• Natural language processing (NLP): While NLP isn’t necessarily a specific algorithm used in speech recognition, it is the area of artificial intelligence which focuses on the interaction between humans and machines through language through speech and text. Many mobile devices incorporate speech recognition into their systems to conduct voice search—e.g. Siri—or provide more accessibility around texting. 
• Hidden markov models (HMM): Hidden Markov Models build on the Markov chain model, which stipulates that the probability of a given state hinges on the current state, not its prior states. While a Markov chain model is useful for observable events, such as text inputs, hidden markov models allow us to incorporate hidden events, such as part-of-speech tags, into a probabilistic model. They are utilized as sequence models within speech recognition, assigning labels to each unit—i.e. words, syllables, sentences, etc.—in the sequence. These labels create a mapping with the provided input, allowing it to determine the most appropriate label sequence.
• N-grams: This is the simplest type of language model (LM), which assigns probabilities to sentences or phrases. An N-gram is sequence of N-words. For example, “order the pizza” is a trigram or 3-gram and “please order the pizza” is a 4-gram. Grammar and the probability of certain word sequences are used to improve recognition and accuracy.
• Neural networks: Primarily leveraged for deep learning algorithms, neural networks process training data by mimicking the interconnectivity of the human brain through layers of nodes. Each node is made up of inputs, weights, a bias (or threshold) and an output. If that output value exceeds a given threshold, it “fires” or activates the node, passing data to the next layer in the network. Neural networks learn this mapping function through supervised learning, adjusting based on the loss function through the process of gradient descent.  While neural networks tend to be more accurate and can accept more data, this comes at a performance efficiency cost as they tend to be slower to train compared to traditional language models.
• Speaker Diarization (SD): Speaker diarization algorithms identify and segment speech by speaker identity. This helps programs better distinguish individuals in a conversation and is frequently applied at call centers distinguishing customers and sales agents.

A wide number of industries are utilizing different applications of speech technology today, helping businesses and consumers save time and even lives. Some examples include:

Automotive: Speech recognizers improves driver safety by enabling voice-activated navigation systems and search capabilities in car radios.

Technology: Virtual agents are increasingly becoming integrated within our daily lives, particularly on our mobile devices. We use voice commands to access them through our smartphones, such as through Google Assistant or Apple’s Siri, for tasks, such as voice search, or through our speakers, via Amazon’s Alexa or Microsoft’s Cortana, to play music. They’ll only continue to integrate into the everyday products that we use, fueling the “Internet of Things” movement.

Healthcare: Doctors and nurses leverage dictation applications to capture and log patient diagnoses and treatment notes.

Sales: Speech recognition technology has a couple of applications in sales. It can help a call center transcribe thousands of phone calls between customers and agents to identify common call patterns and issues. AI chatbots can also talk to people via a webpage, answering common queries and solving basic requests without needing to wait for a contact center agent to be available. It both instances speech recognition systems help reduce time to resolution for consumer issues.

Security: As technology integrates into our daily lives, security protocols are an increasing priority. Voice-based authentication adds a viable level of security.

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Speech Recognition: Everything You Need to Know in 2024

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Speech recognition, also known as automatic speech recognition (ASR) , enables seamless communication between humans and machines. This technology empowers organizations to transform human speech into written text. Speech recognition technology can revolutionize many business applications , including customer service, healthcare, finance and sales.

In this comprehensive guide, we will explain speech recognition, exploring how it works, the algorithms involved, and the use cases of various industries.

If you require training data for your speech recognition system, here is a guide to finding the right speech data collection services.

What is speech recognition?

Speech recognition, also known as automatic speech recognition (ASR), speech-to-text (STT), and computer speech recognition, is a technology that enables a computer to recognize and convert spoken language into text.

Speech recognition technology uses AI and machine learning models to accurately identify and transcribe different accents, dialects, and speech patterns.

What are the features of speech recognition systems?

Speech recognition systems have several components that work together to understand and process human speech. Key features of effective speech recognition are:

• Audio preprocessing: After you have obtained the raw audio signal from an input device, you need to preprocess it to improve the quality of the speech input The main goal of audio preprocessing is to capture relevant speech data by removing any unwanted artifacts and reducing noise.
• Feature extraction: This stage converts the preprocessed audio signal into a more informative representation. This makes raw audio data more manageable for machine learning models in speech recognition systems.
• Language model weighting: Language weighting gives more weight to certain words and phrases, such as product references, in audio and voice signals. This makes those keywords more likely to be recognized in a subsequent speech by speech recognition systems.
• Acoustic modeling : It enables speech recognizers to capture and distinguish phonetic units within a speech signal. Acoustic models are trained on large datasets containing speech samples from a diverse set of speakers with different accents, speaking styles, and backgrounds.
• Speaker labeling: It enables speech recognition applications to determine the identities of multiple speakers in an audio recording. It assigns unique labels to each speaker in an audio recording, allowing the identification of which speaker was speaking at any given time.
• Profanity filtering: The process of removing offensive, inappropriate, or explicit words or phrases from audio data.

What are the different speech recognition algorithms?

Speech recognition uses various algorithms and computation techniques to convert spoken language into written language. The following are some of the most commonly used speech recognition methods:

• Hidden Markov Models (HMMs): Hidden Markov model is a statistical Markov model commonly used in traditional speech recognition systems. HMMs capture the relationship between the acoustic features and model the temporal dynamics of speech signals.
• Estimate the probability of word sequences in the recognized text
• Convert colloquial expressions and abbreviations in a spoken language into a standard written form
• Map phonetic units obtained from acoustic models to their corresponding words in the target language.
• Speaker Diarization (SD): Speaker diarization, or speaker labeling, is the process of identifying and attributing speech segments to their respective speakers (Figure 1). It allows for speaker-specific voice recognition and the identification of individuals in a conversation.

Figure 1: A flowchart illustrating the speaker diarization process

• Dynamic Time Warping (DTW): Speech recognition algorithms use Dynamic Time Warping (DTW) algorithm to find an optimal alignment between two sequences (Figure 2).

Figure 2: A speech recognizer using dynamic time warping to determine the optimal distance between elements

5. Deep neural networks: Neural networks process and transform input data by simulating the non-linear frequency perception of the human auditory system.

6. Connectionist Temporal Classification (CTC): It is a training objective introduced by Alex Graves in 2006. CTC is especially useful for sequence labeling tasks and end-to-end speech recognition systems. It allows the neural network to discover the relationship between input frames and align input frames with output labels.

Speech recognition vs voice recognition

Speech recognition is commonly confused with voice recognition, yet, they refer to distinct concepts. Speech recognition converts  spoken words into written text, focusing on identifying the words and sentences spoken by a user, regardless of the speaker’s identity. 

On the other hand, voice recognition is concerned with recognizing or verifying a speaker’s voice, aiming to determine the identity of an unknown speaker rather than focusing on understanding the content of the speech.

What are the challenges of speech recognition with solutions?

While speech recognition technology offers many benefits, it still faces a number of challenges that need to be addressed. Some of the main limitations of speech recognition include:

Acoustic Challenges:

• Assume a speech recognition model has been primarily trained on American English accents. If a speaker with a strong Scottish accent uses the system, they may encounter difficulties due to pronunciation differences. For example, the word “water” is pronounced differently in both accents. If the system is not familiar with this pronunciation, it may struggle to recognize the word “water.”

Solution: Addressing these challenges is crucial to enhancing  speech recognition applications’ accuracy. To overcome pronunciation variations, it is essential to expand the training data to include samples from speakers with diverse accents. This approach helps the system recognize and understand a broader range of speech patterns.

• For instance, you can use data augmentation techniques to reduce the impact of noise on audio data. Data augmentation helps train speech recognition models with noisy data to improve model accuracy in real-world environments.

Figure 3: Examples of a target sentence (“The clown had a funny face”) in the background noise of babble, car and rain.

Linguistic Challenges:

• Out-of-vocabulary words: Since the speech recognizers model has not been trained on OOV words, they may incorrectly recognize them as different or fail to transcribe them when encountering them.

Figure 4: An example of detecting OOV word

Solution: Word Error Rate (WER) is a common metric that is used to measure the accuracy of a speech recognition or machine translation system. The word error rate can be computed as:

Figure 5: Demonstrating how to calculate word error rate (WER)

• Homophones: Homophones are words that are pronounced identically but have different meanings, such as “to,” “too,” and “two”. Solution: Semantic analysis allows speech recognition programs to select the appropriate homophone based on its intended meaning in a given context. Addressing homophones improves the ability of the speech recognition process to understand and transcribe spoken words accurately.

Technical/System Challenges:

• Data privacy and security: Speech recognition systems involve processing and storing sensitive and personal information, such as financial information. An unauthorized party could use the captured information, leading to privacy breaches.

Solution: You can encrypt sensitive and personal audio information transmitted between the user’s device and the speech recognition software. Another technique for addressing data privacy and security in speech recognition systems is data masking. Data masking algorithms mask and replace sensitive speech data with structurally identical but acoustically different data.

Figure 6: An example of how data masking works

• Limited training data: Limited training data directly impacts  the performance of speech recognition software. With insufficient training data, the speech recognition model may struggle to generalize different accents or recognize less common words.

Solution: To improve the quality and quantity of training data, you can expand the existing dataset using data augmentation and synthetic data generation technologies.

13 speech recognition use cases and applications

In this section, we will explain how speech recognition revolutionizes the communication landscape across industries and changes the way businesses interact with machines.

Customer Service and Support

• Interactive Voice Response (IVR) systems: Interactive voice response (IVR) is a technology that automates the process of routing callers to the appropriate department. It understands customer queries and routes calls to the relevant departments. This reduces the call volume for contact centers and minimizes wait times. IVR systems address simple customer questions without human intervention by employing pre-recorded messages or text-to-speech technology . Automatic Speech Recognition (ASR) allows IVR systems to comprehend and respond to customer inquiries and complaints in real time.
• Customer support automation and chatbots: According to a survey, 78% of consumers interacted with a chatbot in 2022, but 80% of respondents said using chatbots increased their frustration level.
• Sentiment analysis and call monitoring: Speech recognition technology converts spoken content from a call into text. After  speech-to-text processing, natural language processing (NLP) techniques analyze the text and assign a sentiment score to the conversation, such as positive, negative, or neutral. By integrating speech recognition with sentiment analysis, organizations can address issues early on and gain valuable insights into customer preferences.
• Multilingual support: Speech recognition software can be trained in various languages to recognize and transcribe the language spoken by a user accurately. By integrating speech recognition technology into chatbots and Interactive Voice Response (IVR) systems, organizations can overcome language barriers and reach a global audience (Figure 7). Multilingual chatbots and IVR automatically detect the language spoken by a user and switch to the appropriate language model.

Figure 7: Showing how a multilingual chatbot recognizes words in another language

• Customer authentication with voice biometrics: Voice biometrics use speech recognition technologies to analyze a speaker’s voice and extract features such as accent and speed to verify their identity.

Sales and Marketing:

• Virtual sales assistants: Virtual sales assistants are AI-powered chatbots that assist customers with purchasing and communicate with them through voice interactions. Speech recognition allows virtual sales assistants to understand the intent behind spoken language and tailor their responses based on customer preferences.
• Transcription services : Speech recognition software records audio from sales calls and meetings and then converts the spoken words into written text using speech-to-text algorithms.

Automotive:

• Voice-activated controls: Voice-activated controls allow users to interact with devices and applications using voice commands. Drivers can operate features like climate control, phone calls, or navigation systems.
• Voice-assisted navigation: Voice-assisted navigation provides real-time voice-guided directions by utilizing the driver’s voice input for the destination. Drivers can request real-time traffic updates or search for nearby points of interest using voice commands without physical controls.

Healthcare:

• Recording the physician’s dictation
• Transcribing the audio recording into written text using speech recognition technology
• Editing the transcribed text for better accuracy and correcting errors as needed
• Formatting the document in accordance with legal and medical requirements.
• Virtual medical assistants: Virtual medical assistants (VMAs) use speech recognition, natural language processing, and machine learning algorithms to communicate with patients through voice or text. Speech recognition software allows VMAs to respond to voice commands, retrieve information from electronic health records (EHRs) and automate the medical transcription process.
• Electronic Health Records (EHR) integration: Healthcare professionals can use voice commands to navigate the EHR system , access patient data, and enter data into specific fields.

Technology:

• Virtual agents: Virtual agents utilize natural language processing (NLP) and speech recognition technologies to understand spoken language and convert it into text. Speech recognition enables virtual agents to process spoken language in real-time and respond promptly and accurately to user voice commands.

Further reading

• Top 5 Speech Recognition Data Collection Methods in 2023
• Top 11 Speech Recognition Applications in 2023

External Links

• 1. Databricks
• 2. PubMed Central
• 3. Qin, L. (2013). Learning Out-of-vocabulary Words in Automatic Speech Recognition . Carnegie Mellon University.
• 4. Wikipedia

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Essential Guide to Automatic Speech Recognition Technology

Over the past decade, AI-powered speech recognition systems have slowly become part of our everyday lives, from voice search to virtual assistants in contact centers, cars, hospitals, and restaurants. These speech recognition developments are made possible by deep learning advancements.

Developers across many industries now use automatic speech recognition (ASR) to increase business productivity, application efficiency, and even digital accessibility. This post discusses ASR, how it works, use cases, advancements, and more.

What is automatic speech recognition?

Speech recognition technology is capable of converting spoken language (an audio signal) into written text that is often used as a command.

Today’s most advanced software can accurately process varying language dialects and accents. For example, ASR is commonly seen in user-facing applications such as virtual agents, live captioning, and clinical note-taking. Accurate speech transcription is essential for these use cases.

Developers in the speech AI space also use  alternative terminologies  to describe speech recognition such as ASR, speech-to-text (STT), and voice recognition.

ASR is a critical component of  speech AI , which is a suite of technologies designed to help humans converse with computers through voice.

Why natural language processing is used in speech recognition

Developers are often unclear about the role of natural language processing (NLP) models in the ASR pipeline. Aside from being applied in language models, NLP is also used to augment generated transcripts with punctuation and capitalization at the end of the ASR pipeline.

After the transcript is post-processed with NLP, the text is used for downstream language modeling tasks:

• Sentiment analysis
• Text analytics
• Text summarization
• Question answering

Speech recognition algorithms

Speech recognition algorithms can be implemented in a traditional way using statistical algorithms or by using deep learning techniques such as neural networks to convert speech into text.

Traditional ASR algorithms

Hidden Markov models (HMM) and dynamic time warping (DTW) are two such examples of traditional statistical techniques for performing speech recognition.

Using a set of transcribed audio samples, an HMM is trained to predict word sequences by varying the model parameters to maximize the likelihood of the observed audio sequence.

DTW is a dynamic programming algorithm that finds the best possible word sequence by calculating the distance between time series: one representing the unknown speech and others representing the known words.

Deep learning ASR algorithms

For the last few years, developers have been interested in deep learning for speech recognition because statistical algorithms are less accurate. In fact, deep learning algorithms work better at understanding dialects, accents, context, and multiple languages, and they transcribe accurately even in noisy environments.

Some of the most popular state-of-the-art speech recognition acoustic models are Quartznet , Citrinet , and Conformer . In a typical speech recognition pipeline, you can choose and switch any acoustic model that you want based on your use case and performance.

Implementation tools for deep learning models

Several tools are available for developing deep learning speech recognition models and pipelines, including Kaldi , Mozilla DeepSpeech, NVIDIA NeMo , NVIDIA Riva , NVIDIA TAO Toolkit , and services from Google, Amazon, and Microsoft.

Kaldi, DeepSpeech, and NeMo are open-source toolkits that help you build speech recognition models. TAO Toolkit and Riva are closed-source SDKs that help you develop customizable pipelines that can be deployed in production.

Cloud service providers like Google, AWS, and Microsoft offer generic services that you can easily plug and play with.

Deep learning speech recognition pipeline

An ASR pipeline consists of the following components:

• Spectrogram generator that converts raw audio to spectrograms.
• Acoustic model that takes the spectrograms as input and outputs a matrix of probabilities over characters over time.
• Decoder (optionally coupled with a language model) that generates possible sentences from the probability matrix.
• Punctuation and capitalization model that formats the generated text for easier human consumption.

A typical deep learning pipeline for speech recognition includes the following components:

• Data preprocessing
• Neural acoustic model
• Decoder (optionally coupled with an n-gram language model)
• Punctuation and capitalization model

Figure 1 shows an example of a deep learning speech recognition pipeline:.

Datasets are essential in any deep learning application. Neural networks function similarly to the human brain. The more data you use to teach the model, the more it learns. The same is true for the speech recognition pipeline.

A few popular speech recognition datasets are

• LibriSpeech
• Fisher English Training Speech
• Mozilla Common Voice (MCV)
• 2000 HUB 5 English Evaluation Speech
• AN4 (includes recordings of people spelling out addresses and names)
• Aishell-1/AIshell-2 Mandarin speech corpus

Data processing is the first step. It includes data preprocessing and augmentation techniques such as speed/time/noise/impulse perturbation and time stretch augmentation, fast Fourier Transformations (FFT) using windowing, and normalization techniques.

For example, in Figure 2, the mel spectrogram is generated from a raw audio waveform after applying FFT using the windowing technique.

We can also use perturbation techniques to augment the training dataset. Figures 3 and 4 represent techniques like noise perturbation and masking being used to increase the size of the training dataset in order to avoid problems like overfitting.

The output of the data preprocessing stage is a spectrogram/mel spectrogram, which is a visual representation of the strength of the audio signal over time. 

Mel spectrograms are then fed into the next stage: a neural acoustic model . QuartzNet, CitriNet, ContextNet, Conformer-CTC, and Conformer-Transducer are examples of cutting-edge neural acoustic models. Multiple ASR models exist for several reasons, such as the need for real-time performance, higher accuracy, memory size, and compute cost for your use case.

However, Conformer-based models are becoming more popular due to their improved accuracy and ability to comprehend. The acoustic model returns the probability of characters/words at each time stamp.

Figure 5 shows the output of the acoustic model, with time stamps. 

The acoustic model’s output is fed into the decoder along with the language model. Decoders include beam search and greedy decoders, and language models include n-gram language, KenLM, and neural scoring. When it comes to the decoder, it helps to generate top words, which are then passed to language models to predict the correct sentence.

In Figure 6, the decoder selects the next best word based on the probability score. Based on the final highest score, the correct word or sentence is selected and sent to the punctuation and capitalization model.

The ASR pipeline generates text with no punctuation or capitalization.

Finally, a punctuation and capitalization model is used to improve the text quality for better readability. Bidirectional Encoder Representations from Transformers (BERT) models are commonly used to generate punctuated text.

Figure 7 shows a simple example of a before-and-after punctuation and capitalization model.

Speech recognition industry impact

There are many unique applications for ASR . For example, speech recognition could help industries such as finance, telecommunications, and unified communications as a service (UCaaS) to improve customer experience, operational efficiency, and return on investment (ROI).

Speech recognition is applied in the finance industry for applications such as call center agent assist and trade floor transcripts. ASR is used to transcribe conversations between customers and call center agents or trade floor agents. The generated transcriptions can then be analyzed and used to provide real-time recommendations to agents. This adds to an 80% reduction in post-call time.

Furthermore, the generated transcripts are used for downstream tasks:

• Intent and entity recognition

Telecommunications

Contact centers are critical components of the telecommunications industry. With contact center technology, you can reimagine the telecommunications customer center, and speech recognition helps with that.

As previously discussed in the finance call center use case, ASR is used in Telecom contact centers to transcribe conversations between customers and contact center agents to analyze them and recommend call center agents in real time. T-Mobile uses ASR for quick customer resolution , for example.

Unified communications as a software

COVID-19 increased demand for UCaaS solutions, and vendors in the space began focusing on the use of speech AI technologies such as ASR to create more engaging meeting experiences.

For example, ASR can be used to generate live captions in video conferencing meetings. Captions generated can then be used for downstream tasks such as meeting summaries and identifying action items in notes.

Future of ASR technology

Speech recognition is not as easy as it sounds. Developing speech recognition is full of challenges, ranging from accuracy to customization for your use case to real-time performance. On the other hand, businesses and academic institutions are racing to overcome some of these challenges and advance the use of speech recognition capabilities.

ASR challenges

Some of the challenges in developing and deploying speech recognition pipelines in production include the following:

• Lack of tools and SDKs that offer state-of-the-art (SOTA) ASR models makes it difficult for developers to take advantage of the best speech recognition technology.
• Limited customization capabilities that enable developers to fine-tune on domain-specific and context-specific jargon, multiple languages, dialects, and accents to have your applications understand and speak like you
• Restricted deployment support; for example, depending on the use case, the software should be capable of being deployed in any cloud, on-premises, edge, and embedded. 
• Real-time speech recognition pipelines; for instance, in a call center agent assist use case, we cannot wait several seconds for conversations to be transcribed before using them to empower agents.

For more information about the major pain points that developers face when adding speech-to-text capabilities to applications, see Solving Automatic Speech Recognition Deployment Challenges .

ASR advancements

Numerous advancements in speech recognition are occurring on both the research and software development fronts. To begin, research has resulted in the development of several new cutting-edge ASR architectures, E2E speech recognition models, and self-supervised or unsupervised training techniques.

On the software side, there are a few tools that enable quick access to SOTA models, and then there are different sets of tools that enable the deployment of models as services in production. 

Key takeaways

Speech recognition continues to grow in adoption due to its advancements in deep learning-based algorithms that have made ASR as accurate as human recognition. Also, breakthroughs like multilingual ASR help companies make their apps available worldwide, and moving algorithms from cloud to on-device saves money, protects privacy, and speeds up inference.

NVIDIA offers Riva , a speech AI SDK, to address several of the challenges discussed above. With Riva, you can quickly access the latest SOTA research models tailored for production purposes. You can customize these models to your domain and use case, deploy on any cloud, on-premises, edge, or embedded, and run them in real-time for engaging natural interactions.

Learn how your organization can benefit from speech recognition skills with the free ebook, Building Speech AI Applications .

Related resources

• GTC session: Speech AI Demystified
• GTC session: Mastering Speech AI for Multilingual Multimedia Transformation
• GTC session: Human-Like AI Voices: Exploring the Evolution of Voice Technology
• NGC Containers: Domain Specific NeMo ASR Application
• NGC Containers: MATLAB
• Webinar: How Telcos Transform Customer Experiences with Conversational AI

About the Authors

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How Does Voice Recognition Work?

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Sometimes, we find ourselves speaking to our digital devices more than other people. The digital assistants on our devices use voice recognition to understand what we're saying. Because of this, we're able to manage many aspects of our lives just by having a conversation with our phone or smart speaker.

Even though voice recognition is such a large part of our lives, we don't usually think about what makes it work. A lot goes on behind the scenes with voice recognition, so here's a dive into what makes it work.

What Is Voice Recognition?

Modern devices usually come loaded with a digital assistant, a program that uses voice recognition to carry out certain tasks on your device. Voice recognition is a set of algorithms that the assistants use to convert your speech into a digital signal and ascertain what you're saying. Programs like Microsoft Word use voice recognition to help type down words.

The First Voice Recognition System

The first voice recognition system was called the Audrey system. The name was a contraction of "Automated Digit Recognition." Invented in 1952 by Bell Laboratories, Audrey was able to recognize numerical digits. The speaker would say a number, and Audrey would light up one of 10 corresponding lightbulbs.

As groundbreaking as this invention was, it wasn't well received. The computer system itself stood about six feet tall and took up a massive amount of space. Regardless of its size, it could only decipher numbers 0-9. Also, only a person with a specific type of voice could use Audrey, so it was manned primarily by one person.

While it had its faults, Audrey was the first step in a long journey to make voice recognition what it is today. It didn't take long before the next voice recognition system arose, which could understand sequences of words.

Related: How to Lock/Unlock an Android Phone With Your Voice Using Google Assistant

Voice Recognition Begins With Converting the Audio Into a Digital Signal

Voice recognition systems have to go through certain steps to figure out what we're saying. When your device's microphone picks up your audio, it's converted into an electrical current which travels down to the Analog to Digital Converter (ADC). As the name suggests, the ADC converts the electric current (AKA, the analog signal) into a digital binary signal.

As the current flows to the ADC, it takes samples of the current and deciphers its voltage at certain points in time. The voltage at a given point in time is called a sample. Each sample is only several thousandths of a second long. Based on the sample's voltage, the ADC will assign a series of eight binary digits (one byte of data).

The Audio Is Processed for Clarity

In order for the device to better understand the speaker, the audio needs to be processed to improve clarity. The device is sometimes tasked with deciphering speech in a noisy environment; thus, certain filters are placed on the audio to help eliminate background noise. For some voice recognition systems, frequencies that are higher and lower than the human's hearing range are filtered out.

The system doesn't only get rid of unwanted frequencies; certain frequencies in the audio are also emphasized so that the computer can better recognize the voice and separate it from background noise. Some voice recognition systems actually split the audio up into several discrete frequencies.

Related: How to Teach Google Assistant to Pronounce Your Name Correctly

Other aspects, such as the speed and volume of the audio, are adjusted to better match the references audio samples that the voice recognition system uses to compare. These filtration and denoising processes really help improve the overall accuracy.

The Voice Recognition System Then Starts Making Words

There are two popular ways that voice recognition systems analyze speech. One is called the hidden Markov model, and the other method is through neural networks.

The Hidden Markov Model Method

The hidden Markov model is the method employed in most voice recognition systems. An important part of this process is breaking down the spoken words into their phonemes (the smallest element of a language). There's a finite number of phonemes in each language, which is why the hidden Markov model method works so well.

There are around 40 phonemes in the English language. When the voice recognition system identifies one, it determines the probability of what the next one will be.

For example, if the speaker utters the sound "ta," there's a certain probability that the next phoneme will be "p" to form the word "tap." There's also the probability that the next phoneme will be "s," but that's far less likely. If the next phoneme does resemble "p," then the system can assume with high certainty that the word is "tap."

The Neural Network Method

A neural network is like a digital brain that learns much in the same way that a human brain does. Neural networks are instrumental in the progress of artificial intelligence and deep learning.

The type of neural network that voice recognition uses is called a Recurrent Neural Network (RNN). According to GeeksforGeeks , RNN is one where the "output from [the] previous step[s] are fed as input to the current step." This means that when an RNN processes a bit of data, it uses that data to influence what it does with the next bit of data— it essentially learns from experience.

The more an RNN is exposed to a certain language, the more accurate the voice recognition will be. If the system identifies the "ta" sound 100 times, and it's followed by the "p" sound 90 of those times, then the network can basically learn that "p" typically comes after "ta."

Because of this, when the voice recognition system identifies a phoneme, it uses the accrued data to predict which one will likely come next. Because RNNs continuously learn, the more it's used, the more accurate the voice recognition will be.

After the voice recognition system identifies the words (whether with the hidden Marvok model or with an RNN), that information is sent to the processor. The system then carries out the task that it's meant to do.

Voice Recognition Has Become a Staple in Modern Technology

Voice recognition has become a huge part of our modern technological landscape. It's been implemented into several industries and services worldwide; indeed, many people control their entire lives with voice-activated assistants. You can find assistants like Siri loaded onto your Apple watches. What was only a dream back in 1952 has become a reality, and it doesn't seem to be stopping anytime soon.

• Technology Explained
• Voice Commands
• Speech Recognition

A Complete Guide to Speech Recognition Technology

Last Updated June 11, 2021

Here’s everything you need to know about speech recognition technology. History, how it works, how it’s used today, what the future holds, and what it all means for you.

Back in 2008, many of us were captivated by Tony Stark’s virtual butler, J.A.R.V.I.S, in Marvel’s Iron Man movie.

J.A.R.V.I.S. started as a computer interface. It was eventually upgraded to an artificial intelligence system that ran the business and provided global security.

Learn more about our speech data solutions.

J.A.R.V.I.S. opened our eyes – and ears – to the possibilities inherent in speech recognition technology. While we’re maybe not all the way there just yet, advancements are being used in many ways on a wide variety of devices.

Speech recognition technology allows for hands-free control of smartphones, speakers, and even vehicles in a wide variety of languages.

It’s an advancement that’s been dreamt of and worked on for decades. The goal is, quite simply, to make life simpler and safer.

In this guide we are going to take a brief look at the history of speech recognition technology. We’ll start with how it works and some devices that make use of it. Then we’ll examine what might be just around the corner.

History of Speech Recognition Technology

Speech recognition is valuable because it saves consumers and companies time and money.

The average typing speed on a desktop computer is around 40 words per minute. That rate diminishes a bit when it comes to typing on smartphones and mobile devices.

When it comes to speech, though, we can rack up between 125 and 150 words per minute. That’s a drastic increase.

Therefore, speech recognition helps us do everything faster—whether it’s creating a document or talking to an automated customer service agent .

The substance of speech recognition technology is the use of natural language to trigger an action. Modern speech technology began in the 1950s and took off over the decades.

Speech Recognition Through the Years

• 1950s : Bell laboratories developed “Audrey”, a system able to recognize the numbers 1-9 spoken by a single voice.
• 1960s : IBM came up with a device called “Shoebox” that could recognize and differentiate between 16 spoken English words.
• 1970s : The It led to the ‘Harpy’ system at Carnegie Mellon that could understand over 1,000 words.
• 1990s : The advent of personal computing brought quicker processors and opened the door for dictation technology. Bell was at it again with dial-in interactive voice recognition systems.
• 2000s : Speech recognition achieved close to an 80% accuracy rate. Then Google Voice came on the scene, making the technology available to millions of users and allowing Google to collect valuable data.
• 2010s : Apple launched Siri and Amazon came out with Alexa in a bid to compete with Google. This big three continues to lead the charge.

Slowly but surely, developers have moved towards the goal of enabling machines to understand and respond to more and more of our verbalized commands.

Today’s leading speech recognition systems—Google Assistant, Amazon Alexa, and Apple’s Siri—would not be where they are today without the early pioneers who paved the way.

Thanks to the integration of new technologies such as cloud-based processing and continuous improvements made thanks to speech data collection , these speech systems have continuously improved their ability to ‘hear’ and understand a wider variety of words, languages, and accents .

How Does Voice Recognition Work?

Now that we’re surrounded by smart cars, smart home appliances, and voice assistants, it’s easy to take for granted how speech recognition technology works .

Because the simplicity of being able to speak to digital assistants is misleading. Voice recognition is incredibly complicated—even now.

Think about how a child learns a language.

From day one, they hear words being used all around them. Parents speak and their child listens. The child absorbs all kinds of verbal cues: intonation, inflection, syntax, and pronunciation. Their brain is tasked with identifying complex patterns and connections based on how their parents use language.

But whereas human brains are hard-wired to acquire speech, speech recognition developers have to build the hard wiring themselves.

The challenge is building the language-learning mechanism. There are thousands of languages, accents, and dialects to consider, after all.

That’s not to say we aren’t making progress. In early 2020, researchers at Google were finally able to beat human performance on a broad range of language understanding tasks.

Google’s updated model now performs better than humans in labelling sentences and finding the right answers to a question.

Basic Steps

• A microphone transmits the vibrations of a person’s voice into a wavelike electrical signal.
• This signal in turn is converted by the system’s hardware—a computer’s sound card, for examples—into a digital signal.
• The speech recognition software analyzes the digital signal to register phonemes, units of sound that distinguish one word from another in a particular language.
• The phenomes are reconstructed into words.

To pick the correct word, the program must rely on context cues, accomplished through trigram analysis .

This method relies on a database of frequent three-word clusters in which probabilities are assigned that any two words will be followed by a given third word.

Think about the predictive text on your phone’s keyboard. A simple example would be typing “how are” and you phone would suggest “you?” The more you use it, though, the more it gets to know your tendencies and will suggest frequently used phrases.

Speech recognition software works by breaking down the audio of a speech recording into individual sounds, analyzing each sound, using algorithms to find the most probable word fit in that language, and transcribing those sounds into text.

How do companies build speech recognition technology?

A lot of this depends on what you’re trying to achieve and how much you’re willing to invest.

As it stands, there’s no need to start from scratch in terms of coding and acquiring speech data because much of that groundwork has been laid and is available to be built upon.

For instance, you can tap into commercial application programming interfaces (APIs) and access their speech recognition algorithms. The problem, though, is they’re not customizable.

You might instead need to seek out speech data collection that can be accessed quickly and efficiently through an easy-to-use API, such as:

• The Speech-to-text API from Google Cloud
• The  Automatic Speech Recognition  (ASR) system from Nuance
• IBM Watson “Speech to text”  API

From there, you design and develop software to suit your requirements. For example, you might code algorithms and modules using Python

Regional accents and speech impediments can throw off word recognition platforms, and background noise can be difficult to penetrate, not to mention multiple-voice input. In other words, understanding speech is a much bigger challenge than simply recognizing sounds.

Different Models

• Acoustic : Take the waveform of speech and break it up into small fragments to predict the most likely phonemes in the speech.
• Pronunciation : Take the sounds and tie them together to make words, i.e. associate words with their phonetic representations.
• Language : Take the words and tie them together to make sentences, i.e. predict the most likely sequence of words (or text strings) among several a set of text strings.

Algorithms can also combine the predictions of acoustic and language models to offer outputs the most likely text string for a given speech file input.

To further highlight the challenge, speech recognition systems have to be able to distinguish between homophones (words with the same pronunciation but different meanings), to learn the difference between proper names and separate words (“Tim Cook” is a person, not a request for Tim to cook), and more.

After all, speech recognition accuracy is what determines whether voice assistants become a can’t-live-without accessory.

How Voice Assistants Bring Speech Recognition into Everyday Life

Speech recognition technology has grown leaps and bounds in the early 21 st century and has literally come home to roost.

Look around you. There could be a handful of devices at your disposal at this very moment.

Let’s look at a few of the leading options.

Apple’s Siri

Apple’s Siri emerged as the first popular voice assistant after its debut in 2011. Since then, it has been integrated on all iPhones, iPads, the Apple Watch, the HomePod, Mac computers, and Apple TV.

Siri is even used as the key user interface in Apple’s CarPlay infotainment system, as well as the wireless AirPod earbuds, and the HomePod Mini.

Siri is with you everywhere you go; on the road, in your home, and for some, literally on your body. This gave Apple a huge advantage in terms of early adoption.

Naturally, being the earliest quite often means receiving most of the flack for functionality that might not work as expected.

Although Apple had a big head start with Siri, many users expressed frustration at its seeming inability to properly understand and interpret voice commands.

If you asked Siri to send a text message or make a call on your behalf, it could easily do so. However, when it came to interacting with third-party apps, Siri was a little less robust compared to its competitors.

But today, an iPhone user can say, “Hey Siri, I’d like a ride to the airport” or “Hey Siri, order me a car,” and Siri will open whatever ride service app you have on your phone and book the trip.

Focusing on the system’s ability to handle follow-up questions, language translation, and revamping Siri’s voice to something more human-esque is helping to iron out the voice assistant’s user experience.

As of 2021, Apple hovers over its competitors in terms of availability by country and thus in Siri’s understanding of foreign accents. Siri is available in more than 30 countries and 21 languages – and, in some cases, several different dialects.

Amazon Alexa

Amazon announced Alexa and the Echo to the world in 2014, kicking off the age of the smart speaker.

Alexa is now housed inside the following:

• Smart speaker
• Show (a voice-controlled tablet)
• Spot (a voice-controlled alarm clock)
• Buds headphones (Amazon’s version of Apple’s AirPods).

In contrast to Apple, Amazon has always believed  the voice assistant with the most “skills”, (its term for voice apps on its Echo assistant devices) “will gain a loyal following, even if it sometimes makes mistakes and takes more effort to use”.

Although some users pegged Alexa’s word recognition rate as being a shade behind other voice platforms, the good news is that Alexa adapts to your voice over time, offsetting any issues it may have with your particular accent or dialect.

Speaking of skills, Amazon’s Alexa Skills Kit (ASK) is perhaps what has propelled Alexa forward as a bonafide platform. ASK allows third-party developers to create apps and tap into the power of Alexa without ever needing native support.

Alexa was ahead of the curve with its integration with smart home devices. They had cameras, door locks, entertainment systems, lighting, and thermostats.

Ultimately, giving users absolute control of their home whether they’re cozying up on their couch or on-the-go. With Amazon’s Smart Home Skill API , you can enable customers to control their connected devices from tens of millions of Alexa-enabled endpoints.

When you ask Siri to add something to your shopping list, she adds it without buying it for you. Alexa however goes a step further.

If you ask Alexa to re-order garbage bags, she’ll scroll Amazon and order some. In fact, you can order millions of products off Amazon without ever lifting a finger; a natural and unique ability that Alexa has over its competitors.

Google Assistant

How many of us have said or heard “let me Google that for you”? Almost everyone, it seems. It only makes sense then, that Google Assistant prevails when it comes to answering (and understanding) all questions its users may have.

From asking for a phrase to be translated into another language, to converting the number of sticks of butter in one cup, Google Assistant not only answers correctly, but also gives some additional context and cites a source website for the information.

Given that it’s backed by Google’s powerful search technology, perhaps it’s an unsurprising caveat.

Though Amazon’s Alexa was released (through the introduction of Echo) two years earlier than Google Home, Google has made great strides in catching up with Alexa in a very short time. Google Home was released in late 2016, and within a year, had already established itself as the most meaningful opponent to Alexa.

In 2017, Google boasted a 95% word accuracy rate for U.S. English, the highest out of all the voice-assistants currently out there. This translates to a 4.9%-word error rate – making Google the first of the group to fall below the 5% threshold.

Word-error rate has its limitations , though. Factors that affect the data include:

• Background noise

Still, they’re getting close to 0% and that’s significant.

To get a better sense of the languages supported by these voice assistants, be sure to check out our comparison article .

Where else is speech recognition technology prevalent?

Voice assistants are far from the only mechanisms through which advancements in speech recognition are becoming even more mainstream.

In-Car Speech Recognition

Voice-activated devices and digital voice assistants aren’t just about making things easier. It’s also about safety – at least it is when it comes to in-car speech recognition .

Companies like Apple, Google, and Nuance have completely reshaped the driver’s experience in their vehicle—aiming at removing the distraction of looking down at your mobile phone while you drive allows drivers to keep their eyes on the road.

• Instead of texting while driving, you can now tell your car who to call or what restaurant to navigate to.
• Instead of scrolling through Apple Music to find your favorite playlist, you can just ask Siri to find and play it for you.
• If the fuel in your car is running low, your in-car speech system can not only inform you that you need to refuel, but also point out the nearest fuel station and ask whether you have a preference for a particular brand. Or perhaps it can warn you that the petrol station you prefer is too far to reach with the fuel remaining.

When it comes to safety, there’s an important caveat to be aware of. A report published by the UK’s Transport Research Laboratory (TRL) showed that driver distraction levels are much lower when using voice activated system technologies compared to touch screen systems.

However, it recommends that further research is necessary to steer the use of spoken instructions as the safest method for future in-car control, seeing as the most effective safety precautions would be the elimination of distractions altogether.

That’s where field data collection comes in.

How to Train a Car

Companies need precise and comprehensive data with respect to terms and phrases that would be used to communicate in a vehicle.

Field data collection is conducted in a specifically chosen physical location or environment as opposed to remotely . This data is collected via loosely structured scenarios that include elements like culture, education, dialect, and social environment that can an impact on how a user will articulate a request.

This is best suited for projects with specific environmental requirements, such as specific acoustics for sound recordings.

Think about in-car speech recognition , for example. Driving around presents very unique circumstances in terms of speech data.

You must be able to record speech data from the cabin of a car to simulate acoustic environment, background noises, and voice commands used in real scenarios.

That’s how you reach new levels of innovation in human and machine interaction.

Voice-Activated Video Games

Speech recognition technology is also making strides in the gaming industry.

Voice-activated video games have begun to extend from the classic console and PC format to voice-activated mobile games and apps .

Creating a video game is already extraordinarily difficult. It takes years to properly flesh out the plot, the gameplay, character development, customizable gear, worlds, and so on. The game also has to be able to change and adapt based on each player’s actions.

Now, just imagine adding another layer to gaming through speech recognition technology.

Many of the companies championing this idea do so with the intention of making gaming more accessible for visually and/or physically impaired players, as well as allowing players to immerse themselves further into gameplay through enabling yet another layer of integration.

Voice control could also potentially lower the learning curve for beginners, seeing as less importance will be placed on figuring out controls. Players can just begin talking right away.

Moving forward, text-to-speech (TTS), synthetic voices, and generative neural networks will help developers create spoken and dynamic dialogue .

You will be able to have a conversation with characters within the game itself.

The rise of speech technology in video games has only just begun.

Speech Recognition Technology: The Focus Moving Forward

What does the future of speech recognition hold?

Here are a few key areas of focus you can expect moving forward.

1. Mobile app voice integration

Integrating voice-tech into mobile apps has become a hot trend, and will remain so because speech is a natural user interface (NUI).

Voice-powered apps increase functionality and save users from complicated navigation.

It’s easier for the user to navigate an app — even if they don’t know the exact name of the item they’re looking for or where to find it in the app’s menu.

Voice integration will soon become a standard that users will expect.

2. Individualized experiences

Voice assistants will also continue to offer more individualized experiences as they get better at differentiating between voices.

Google Home, for example, can not only support up to six user accounts but also detect unique voices, which allows you to customize many features.

You can ask “What’s on my calendar today?” or “tell me about my day?” and the assistant will dictate commute times, weather, and news information tailored specifically to you.

It also includes features such as nicknames, work locations, payment information, and linked accounts such as Google Play, Spotify, and Netflix.

Similarly, for those using Alexa, saying “learn my voice” will allow you to create separate voice profiles so it can detect who is speaking.

3. Smart displays

The smart speaker is great and all, but what people are really after now is the smart display, essentially a smart speaker with a touch screen attached to it.

In 2020, the sale of smart displays rose by 21% to 9.5 million units, while basic smart speakers fell by 3%, and that trend is only likely to continue.

Smart displays like the Russian Sber portal or the Chinese smart screen Xiaodu, for example, are already equipped with several AI-powered functions, including far-field voice interaction, facial recognition, hand gesture control, and eye gesture detection.

Collect Better Data

We help you create outstanding human experiences with high-quality speech, image, video, or text data for AI.

Summa Linguae Technologies collects and annotates the training and testing data you need to build your AI-powered solutions, including voice assistants, wearables, autonomous vehicles, and much more.

We offer both in-field and remote data collection options. They’re backed by a network of technical engineers, project managers, quality assurance professionals, and annotators.

Here are a few resources you can tap into right away:

• Data Sets – Sample of our pre-packaged speech ,  image , and  video data sets. These data samples are free to download and provide a preview of the capabilities of our ready-to-order or highly customizable data solutions .
• The Ultimate Guide to Data Collection (PDF) – Learn how to collect data for emerging technology.

Want even more? Contact us today for a full speech data solutions consultation.

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Speech Recognition AI: What is it and How Does it Work|Gnani

A Beginner’s Guide to Speech Recognition AI

AI speech recognition is a technology that allows computers and applications to understand human speech data . It is a feature that has been around for decades, but it has increased in accuracy and sophistication in recent years.

Speech recognition works by using artificial intelligence to  recognize the words or language that a person speaks and then translate that content into text. It’s important to note that this technology is still in its infancy but is improving its accuracy rapidly.

What is Speech Recognition AI?

Speech recognition enables computers , applications and software to comprehend and translate human speech data into text  for business solutions . The speech recognition model works by using artificial intelligence (AI) to analyze your voice and language , identify by learning the words you are saying, and then output those words with transcription accuracy as model content or text data on a screen.

Speech Recognition in AI

Speech recognition is a significant part of artificial intelligence (AI) applications . AI is a machine’s ability to mimic human behaviour by learning from its environment. Speech recognition enables computers and software applications to “understand” what people are saying, which allows them to process information faster and with high accuracy. Speech recognition is also used as models in voice assistants like Siri and Alexa, which allow users to interact with computers using natural transcription language data or content .

Thanks to recent advancements, speech recognition technology is now more precise and widely used than in the past. It is used in various fields, including healthcare, customer service, education, and entertainment. However, there are still challenges to overcome, such as better handling of accents and dialects and the difficulty of recognizing speech in noisy environments. Despite these challenges, speech recognition is an exciting area of artificial intelligence with great potential for future development.

How Does Speech Recognition AI Work?

Speech recognition or voice recognition is a complex process that involves audio accuracy over several steps and data or language solutions , including:

• Recognizing the words , models and content in the user’s speech or audio . This business accuracy step requires training the model to identify each word in your vocabulary or audio cloud .
• Converting those audios and language into text. This step involves converting recognized audios i nto letters or numbers (called phonemes) so that other parts of the AI software solutions system can process th ose models .
• Determining what was said. Next, AI looks at which content and words were spoken most often and how frequently they were used together to determine their meaning (this process is known as “predictive modelling”).
• Parsing out commands from the rest of your speech or audio content (also known as disambiguation).

Speech Recognition AI and Natural Language Processing

Natural Language Processing is a part of artificial intelligence that involves analyzing data related to natural language and converting it into a machine- comprehendible format. Speech recognition and AI play a pivotal role in NLPs in improving the accuracy and efficiency of human language recognition. 

A lot of businesses now include speech-to-text software or speech recognition AI to enhance their business applications and improve customer experience. By using speech recognition AI and natural language processing together, companies can transcribe calls, meetings etc. Giant companies like Apple, Google, and Amazon are leveraging AI-based speech or voice recognition applications to provide a flawless customer experience. 

Use Cases of Speech Recognition AI

Speech recognition AI is being used as business solutions in many industries and applications . From ATMs to call centers and voice-activated audio content assistants, AI is helping people interact with technology and software more naturally with better data transcription accuracy than ever before.

Call Centers

Speech recognition is one of the most popular uses of speech AI in call centers. This technology allows you to listen to what customers are saying and then use that information via cloud models to respond appropriately.

You can also use speech recognition technology for voice or audio biometrics, which means using voice patterns as proof of identity or authorization for access solutions or services without relying on passwords or other traditional methods or models like fingerprints or eye scans. This can eliminate business issues like forgotten passwords or compromised security codes in favor of something more secure: your voice!

Banking and financial institutions are using speech AI applications to help customers with their business queries. For example, you can ask a bank about your account balance or the current interest rate on your savings account. This cuts down on the time it takes for customer service representatives to answer questions they would typically have to research and look at cloud data , which means quicker response times and better customer service.

Telecommunications

Speech-enabled AI is a technology that’s gaining traction in the telecommunications industry. Speech recognition technology models enable calls to be analyzed and managed more efficiently. This allows agents to focus on their highest-value tasks to deliver better customer service.

Customers can now interact with businesses in real-time 24/7 via voice transcription solutions or text messaging applications , which makes them feel more connected with the company and improves their overall experience.

Speech AI is a learning technology used in many different areas as transcription solutions . Healthcare is one of the most important, as it can help doctors and nurses care for their patients better. Voice-activated devices use learning models that allow patients to communicate with doctors, nurses, and other healthcare professionals without using their hands or typing on a keyboard.

Doctors can use speech recognition AI via cloud data to help patients understand their feelings and why they feel that way. It’s much easier than having them read through a brochure or pamphlet—and it’s more engaging. Speech AI can also take down patient histories and help with medical transcriptions.

Media and Marketing

Tools such as dictation software use speech recognition and AI to help users type or write more in much less time. Roughly speaking, copywriters and content writers can transcribe as much as 3000-4000 words in as less as half an hour on an average.

Accuracy, though, is a factor. These tools don’t guarantee 100% foolproof transcription. Still, they are extremely beneficial in helping media and marketing people in composing their first drafts.

Challenges in Working with Speech Recognition AI

There are many challenges in working with speech AI. For example, both technology and cloud are new and developing rapidly. As a result, it isn’t easy to make accurate predictions about how long it will take for a company to build its speech-enabled product.

Another challenge with speech AI is getting the right tools to analyze your data. Most people need access to this technology or cloud , so finding the right tool for your requirements may take time and effort.

You must use the correct language and syntax when creating your algorithms on cloud . This can be difficult because it requires understanding how computers and humans communicate. Speech recognition still needs improvement, and it can be difficult for computers to understand every word you say.

If you use speech recognition software, you will need to train it on your voice before it can understand what you’re saying. This can take a long time and requires careful study of how your voice sounds different from other people’s.

The other concern is that there are privacy laws surrounding medical records. These laws vary from state to state, so you’ll need to check with your jurisdiction before implementing speech AI technology.

Educating your staff on the technology and how it works is important if you decide to use speech AI. This will help them understand what they’re recording and why they’re recording it.

Frequently Asked Questions

How does speech recognition work.

Speech recognition AI is the process of converting spoken language into text. The technology uses machine learning and neural networks to process audio data and convert it into words that can be used in businesses.

What is the purpose of speech recognition AI ?

Speech recognition AI can be used for various purposes, including dictation and transcription. The technology is also used in voice assistants like Siri and Alexa.

What is speech communication in AI?

Speech communication is using speech recognition and speech synthesis to communicate with a computer. Speech recognition can allow users to dictate text into a program, saving time compared to typing it out. Speech synthesis is used for chatbots and voice assistants  like Siri and Alexa.

Which type of AI is used in speech recognition?

AI and machine learning are used in advanced speech recognition software, which processes speech through grammar, structure, and syntax.

What are the difficulties in voice recognition AI in artificial intelligence?

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What Is Speech Recognition and How Does It Work?

With modern devices, you can check the weather, place an order, make a call, and play your favorite song entirely hands-free. Giving voice commands to your gadgets makes it incredibly easy to multitask and handle daily chores. It’s all possible thanks to speech recognition technology.

Let’s explore speech recognition further to understand how it has evolved, how it works, and where it’s used today.

What Is Speech Recognition?

Speech recognition is the capacity of a computer to convert human speech into written text. Also known as automatic/automated speech recognition (ASR) and speech to text (STT), it’s a subfield of computer science and computational linguistics. Today, this technology has evolved to the point where machines can understand natural speech in different languages, dialects, accents, and speech patterns.

Speech Recognition vs. Voice Recognition

Although similar, speech and voice recognition are not the same technology. Here’s a breakdown below.

Speech recognition aims to identify spoken words and turn them into written text, in contrast to voice recognition which identifies an individual’s voice. Essentially, voice recognition recognizes the speaker, while speech recognition recognizes the words that have been spoken. Voice recognition is often used for security reasons, such as voice biometrics. And speech recognition is implemented to identify spoken words, regardless of who the speaker is.

History of Speech Recognition

You might be surprised that the first speech recognition technology was created in the 1950s. Browsing through the history of the technology gives us interesting insights into how it has evolved, gradually increasing vocabulary size and processing speed.

1952: The first speech recognition software was “Audrey,” developed by Bell Labs, which could recognize spoken numbers from 0 to 9.

1960s: At the Radio Research Lab in Tokyo, Suzuki and Nakata built a machine able to recognize vowels.

1962: The next breakthrough was IBM’s “Shoebox,” which could identify 16 different words.

1976: The “ Harpy ” speech recognition system at Carnegie-Mellon University could understand over 1,000 words.

Mid-1980s: Fred Jelinek's research team developed a voice-activated typewriter, Tangora, with an expanded bandwidth of 20,000 words.

1992: Developed at Bell Labs, AT&T’s Voice Recognition Call Processing service was able to route phone calls without a human operator.

2007: Google started working on its first speech recognition software, which led to the creation of Google Voice Search in 2012.

2010s: Apple’s Siri and Amazon Alexa came into the scene, making speech recognition software easily available to the masses. 

How Does Speech Recognition Work?

We’re used to the simplicity of operating a gadget through voice, but we’re usually unaware of the complex processes taking place behind the scenes.

Speech recognition systems incorporate linguistics, mathematics, deep learning, and statistics to process spoken language. The software uses statistical models or neural networks to convert the speech input into word output. The role of natural language processing (NLP) is also significant, as it’s implemented to return relevant text to the given voice command.

Computers go through the following steps to interpret human speech:

• The microphone translates sound vibrations into electrical signals.
• The computer then digitizes the received signals.
• Speech recognition software analyzes digital signals to identify sounds and distinguish phonemes (the smallest units of speech).
• Algorithms match the signals with suitable text that represents the sounds.

This process gets more complicated when you account for background noise, context, accents, slang, cross talk, and other influencing factors. With the application of artificial intelligence and  machine learning , speech recognition technology processes voice interactions to improve performance and precision over time.

Speech Recognition Key Features

Here are the key features that enable speech recognition systems to function:

• Language weighting: This feature gives weight to certain words and phrases over others to better respond in a given context. For instance, you can train the software to pay attention to industry or product-specific words.
• Speaker labeling: It labels all speakers in a group conversation to note their individual contributions.
• Profanity filtering: Recognizes and filters inappropriate words to disallow unwanted language.
• Acoustics training: Distinguishes ambient noise, speaker style, pace, and volume to tune out distractions. This feature comes in handy in busy call centers and office spaces.

Speech Recognition Benefits

Speech recognition has various advantages to offer to businesses and individuals alike. Below are just a few of them. 

Faster Communication

Communicating through voice rather than typing every individual letter speeds up the process significantly. This is true both for interpersonal and human-to-machine communication. Think about how often you turn to your phone assistant to send a text message or make a call.

Multitasking

Completing actions hands-free gives us the opportunity to handle multiple tasks at once, which is a huge benefit in our busy, fast-paced lives. Voice search , for example, allows us to look up information anytime, anywhere, and even have the assistant read out the text for us.

Aid for Hearing and Visual Impairments

Speech-to-text and text-to-speech systems are of substantial importance to people with visual impairments. Similarly, users with hearing difficulties rely on audio transcription software to understand speech. Tools like Google Meet can even provide captions in different languages by translating the speech in real-time.

Real-Life Applications of Speech Recognition

The practical applications of speech recognition span various industries and areas of life. Speech recognition has become prominent both in personal and business use.

• Technology: Mobile assistants, smart home devices, and self-driving cars have ceased to be sci-fi fantasies thanks to the advancement of speech recognition technology. Apple, Google, Microsoft, Amazon, and many others have succeeded in building powerful software that’s now closely integrated into our daily lives.
• Education: The easy conversion between verbal and written language aids students in learning information in their preferred format. Speech recognition assists with many academic tasks, from planning and completing assignments to practicing new languages. 
• Customer Service:  Virtual assistants capable of speech recognition can process spoken queries from customers and identify the intent. Hoory is an example of an assistant that converts speech to text and vice versa to listen to user questions and read responses out loud.

Speech Recognition Summarized

Speech recognition allows us to operate and communicate with machines through voice. Behind the scenes, there are complex speech recognition algorithms that enable such interactions. As the algorithms become more sophisticated, we get better software that recognizes various speech patterns, dialects, and even languages.

Faster communication, hands-free operations, and hearing/visual impairment aid are some of the technology's biggest impacts. But there’s much more to expect from speech-activated software, considering the incredible rate at which it keeps growing.

Ultimate Guide To Speech Recognition Technology (2023)

• April 12, 2023

SpeakWrite Blog

Learn about speech recognition technology—how speech to text software works, benefits, limitations, transcriptions, and other real world applications..

Whether you’re a professional in need of more efficient transcription solutions or simply want your voice-enabled device to work smarter for you, this guide to speech recognition technology is here with all the answers.

Few technologies have evolved rapidly in recent years as speech recognition. In just the last decade, speech recognition has become something we rely on daily. From voice texting to Amazon Alexa understanding natural language queries, it’s hard to imagine life without speech recognition software.

But before deep learning was ever a word people knew, mid-century were engineers paving the path for today’s rapidly advancing world of automatic speech recognition. So let’s take a look at how speech recognition technologies evolved and speech-to-text became king.

What Is Speech Recognition Technology?

With machine intelligence and deep learning advances, speech recognition technology has become increasingly popular. Simply put, speech recognition technology (otherwise known as speech-to-text or automatic speech recognition) is software that can convert the sound waves of spoken human language into readable text. These programs match sounds to word sequences through a series of steps that include:

• Pre-processing: may consist of efforts to improve the audio of speech input by reducing and filtering the noise to reduce the error rate
• Feature extraction: this is the part where sound waves and acoustic signals are transformed into digital signals for processing using specialized speech technologies.
• Classification: extracted features are used to find spoken text; machine learning features can refine this process.
• Language modeling: considers important semantic and grammatical rules of a language while creating text.

How Does Speech Recognition Technology Work?

Speech recognition technology combines complex algorithms and language models to produce word output humans can understand. Features such as frequency, pitch, and loudness can then be used to recognize spoken words and phrases.

Here are some of the most common models for speech recognition, which include acoustic models and language models . Sometimes, several of these are interconnected and work together to create higher-quality speech recognition software and applications.

Natural Language Processing (NLP)

“Hey, Siri, how does speech-to-text work?”

Try it—you’ll likely hear your digital assistant read a sentence or two from a relevant article she finds online, all thanks to the magic of natural language processing.

Natural language processing is the artificial intelligence that gives machines like Siri the ability to understand and answer human questions. These AI systems enable devices to understand what humans are saying, including everything from intent to parts of speech.

But NLP is used by more than just digital assistants like Siri or Alexa—it’s how your inbox knows which spam messages to filter, how search engines know which websites to offer in response to a query, and how your phone knows which words to autocomplete.

Neural Networks

Neural networks are one of the most powerful AI applications in speech recognition. They’re used to recognize patterns and process large amounts of data quickly.

For example, neural networks can learn from past input to better understand what words or phrases you might use in a conversation. It uses those patterns to more accurately detect the words you’re saying.

Leveraging cutting-edge deep learning algorithms, neural networks are revolutionizing how machines recognize speech commands. By imitating neurons in our brains and creating intricate webs of electrochemical connections between them, these robust architectures can process data with unparalleled accuracy for various applications such as automatic speech recognition.

Hidden Markov Models (HMM)

The Hidden Markov Model is a powerful tool for acoustic modeling, providing strong analytical capabilities to accurately detect natural speech. Its application in the field of Natural Language Processing has allowed researchers to efficiently train machines on word generation tasks, acoustics, and syntax to create unified probabilistic models.

Speaker Diarization

Speaker diarization is an innovative process that segments audio streams into distinguishable speakers, allowing the automatic speech recognition transcript to organize each speaker’s contributions separately. Using unique sound qualities and word patterns, this technique pinpoints conversations accurately so every voice can be heard.

The History of Speech Recognition Technology

It’s hard to believe that just a few short decades ago, the idea of having a computer respond to speech felt like something straight out of science fiction. Yet, Fast-forward to today, and voice-recognition technology has gone from being an obscure concept to becoming so commonplace you can find it in our smartphones.

But where did this all start? First, let’s take a look at the history of speech recognition technology – from its uncertain early days through its evolution into today’s easy-to-use technology.

Speech recognition technology has existed since the 1950s when Bell Laboratory researchers first developed systems to recognize simple commands . However, early speech recognition systems were limited in their capabilities and could not identify more complex phrases or sentences.

In the 1980s, advances in computing power enabled the development of better speech recognition systems that could understand entire sentences. Today, speech recognition technology has become much more advanced, with some systems able to recognize multiple languages and dialects with high accuracy.

Timeline of Speech Recognition Programs

• 1952 – Bell Labs researchers created “Audrey,” an innovative system for recognizing individual digits. Early speech recognition systems were limited in their capabilities and could not identify more complex phrases or sentences.
• 1962 – IBM shook the tech sphere in 1962 at The World’s Fair, showcasing a remarkable 16-word speech recognition capability – nicknamed “Shoebox” —that left onlookers awestruck.
• 1980s – IBM revolutionized the typewriting industry in the 1980s with Tangora , a voice-activated system that could understand up to 20,000 words. Advances in computing power enabled the development of better speech recognition systems that could understand entire sentences.
• 1996 – IBM’s VoiceType Simply Speaking application recognized 42,000 English and Spanish words.
• 2007 – Google launched GOOG-411 as a telephone directory service, an endeavor that provided immense amounts of data for improving speech recognition systems over time. Now, this technology is available across 30 languages through Google Voice Search .
• 2017 – Microsoft made history when its research team achieved the remarkable goal of transcribing phone conversations utilizing various deep-learning models.

How is Speech Recognition Used Today?

Speech recognition technology has come a long way since its inception at Bell Laboratories.

Today, speech recognition technology has become much more advanced, with some systems able to recognize multiple languages and dialects with high accuracy and low error rates.

Speech recognition technology is used in a wide range of applications in our daily lives, including:

• Voice Texting: Voice texting is a popular feature on many smartphones that allow users to compose text messages without typing.
• Smart Home Automation: Smart home systems use voice commands technology to control lights, thermostats, and other household appliances with simple commands.
• Voice Search: Voice search is one of the most popular applications of speech recognition, as it allows users to quickly
• Transcription: Speech recognition technology can transcribe spoken words into text fast.
• Military and Civilian Vehicle Systems: Speech recognition technology can be used to control unmanned aerial vehicles, military drones, and other autonomous vehicles.
• Medical Documentation: Speech recognition technology is used to quickly and accurately transcribe medical notes, making it easier for doctors to document patient visits.

Key Features of Advanced Speech Recognition Programs

If you’re looking for speech recognition technology with exceptional accuracy that can do more than transcribe phonetic sounds, be sure it includes these features.

Acoustic training

Advanced speech recognition programs use acoustic training models to detect natural language patterns and better understand the speaker’s intent. In addition, acoustic training can teach AI systems to tune out ambient noise, such as the background noise of other voices.

Speaker labeling

Speaker labeling is a feature that allows speech recognition systems to differentiate between multiple speakers, even if they are speaking in the same language. This technology can help keep track of who said what during meetings and conferences, eliminating the need for manual transcription.

Dictionary customization

Advanced speech recognition programs allow users to customize their own dictionaries and include specialized terminology to improve accuracy. This can be especially useful for medical professionals who need accurate documentation of patient visits.

If you don’t want your transcript to include any naughty words, then you’ll want to make sure your speech recognition system consists of a filtering feature. Filtering allows users to specify which words should be filtered out of their transcripts, ensuring that they are clean and professional.

Language weighting

Language weighting is a feature used by advanced speech recognition systems to prioritize certain commonly used words over others. For example, this feature can be helpful when there are two similar words, such as “form” and “from,” so the system knows which one is being spoken.

The Benefits of Speech Recognition Technology

Human speech recognition technology has revolutionized how people navigate, purchase, and communicate. Additionally, speech-to-text technology provides a vital bridge to communication for individuals with sight and auditory disabilities. Innovations like screen readers, text-to-speech dictation systems, and audio transcriptions help make the world more accessible to those who need it most.

Limits of Speech Recognition Programs

Despite its advantages, speech recognition technology still needs to be improved.

• Accuracy rate and reliability – the quality of the audio signal and the complexity of the language being spoken can significantly impact the system’s ability to accurately interpret spoken words. For now, speech-to-text technology has a higher average error rate than humans.
• Formatting – Exporting speech recognition results into a readable format, such as Word or Excel, can be difficult and time-consuming—especially if you must adhere to professional formatting standards.
• Ambient noise – Speech recognition systems are still incapable of reliably recognizing speech in noisy environments. If you plan on recording yourself and turning it into a transcript later, make sure the environment is quiet and free from distractions.
• Translation – Human speech and language are difficult to translate word for word, as things like syntax, context, and cultural differences can lead to subtle meanings that are lost in direct speech-to-text translations.
• Security – While speech recognition systems are great for controlling devices, you don’t always have control over how your data is stored and used once recorded.

Using Speech Recognition for Transcriptions

Speech recognition technology is commonly used to transcribe audio recordings into text documents and has become a standard tool in business and law enforcement. There are handy apps like Otter.ai that can help you quickly and accurately transcribe and summarize meetings and speech-to-text features embedded in document processors like Word.

However, you should use speech recognition technology for transcriptions with caution because there are a number of limitations that could lead to costly mistakes.

If you’re creating an important legal document or professional transcription , relying on speech recognition technology or any artificial intelligence to provide accurate results is not recommended. Instead, it’s best to employ a professional transcription service or hire an experienced typist to accurately transcribe audio recordings.

Human typists have an accuracy level of 99% – 100%, can follow dictation instructions, and can format your transcript appropriately depending on your instructions. As a result, there is no need for additional editing once your document is delivered (usually in 3 hours or less), and you can put your document to use immediately.

Unfortunately, speech recognition technology can’t achieve these things yet. You can expect an accuracy of up to 80% and little to no professional formatting. Additionally, your dictation instructions will fall on deaf “ears.” Frustratingly, they’ll just be included in the transcription rather than followed to a T. You’ll wind up spending extra time editing your transcript for readability, accuracy, and professionalism.

So if you’re looking for dependable, accurate, fast transcriptions, consider human transcription services instead.

Frequently Asked Questions

Is speech recognition technology accurate.

The accuracy of speech recognition technology depends on several factors, including the quality of the audio signal, the complexity of the language being spoken, and the specific algorithms used by the system.

Some speech recognition software can withstand poor acoustic quality, identify multiple speakers, understand accents, and even learn industry jargon. Others are more rudimentary and may have limited vocabulary or may only be able to work with pristine audio quality.

Speaker identification vs. speech recognition: What’s the difference?

The two are often used interchangeably. However, there is a distinction. Speech recognition technology shouldn’t be confused with speech identification technology, which identifies who is speaking rather than what the speaker has to say.

What type of technology is speech recognition?

Speech recognition is a type of technology that allows computers to understand and interpret spoken words. It is a form of artificial intelligence (AI) that uses algorithms to recognize patterns in audio signals, such as the sound of speech. Speech recognition technology has been around for decades.

Is speech recognition AI technology?

Yes, speech recognition is a form of artificial intelligence (AI) that uses algorithms to recognize patterns in audio signals, such as the sound of speech. Speech recognition technology has been around for decades, but it wasn’t until recently that systems became sophisticated enough to accurately understand and interpret spoken words.

What are examples of speech recognition devices?

Examples of speech recognition devices include virtual assistants such as Amazon Alexa, Google Assistant, and Apple Siri. Additionally, many mobile phones and computers now come with built-in voice recognition software that can be used to control the device or issue commands. Speech recognition technology is also used in various other applications, such as automated customer service systems, medical transcription software, and real-time language translation systems.

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This wide-ranging guide to artificial intelligence in the enterprise provides the building blocks for becoming successful business consumers of AI technologies. It starts with introductory explanations of AI's history, how AI works and the main types of AI. The importance and impact of AI is covered next, followed by information on AI's key benefits and risks, current and potential AI use cases, building a successful AI strategy, steps for implementing AI tools in the enterprise and technological breakthroughs that are driving the field forward. Throughout the guide, we include hyperlinks to TechTarget articles that provide more detail and insights on the topics discussed.

Voice recognition (speaker recognition).

• Alexander S. Gillis, Technical Writer and Editor

What is voice recognition (speaker recognition)?

Voice or speaker recognition is the ability of a machine or program to receive and interpret dictation or to understand and perform spoken commands. Voice recognition has gained prominence and use with the rise of artificial intelligence ( AI ) and intelligent assistants, such as Amazon's Alexa and Apple's Siri .

Voice recognition systems let consumers interact with technology simply by speaking to it, enabling hands-free requests, reminders and other simple tasks.

Voice recognition can identify and distinguish voices using automatic speech recognition (ASR) software programs. Some ASR programs require users first train the program to recognize their voice for a more accurate speech-to-text conversion. Voice recognition systems evaluate a voice's frequency, accent and flow of speech.

Although voice recognition and speech recognition are referred to interchangeably, they aren't the same, and a critical distinction must be made. Voice recognition identifies the speaker, whereas speech recognition evaluates what is said.

This article is part of

A guide to artificial intelligence in the enterprise

• Which also includes:
• 10 top AI and machine learning trends for 2024
• 10 top artificial intelligence certifications and courses for 2024
• The future of AI: What to expect in the next 5 years

How does voice recognition work?

Voice recognition software on computers requires analog audio to be converted into digital signals, known as analog-to-digital (A/D) conversion. For a computer to decipher a signal, it must have a digital database of words or syllables as well as a quick process for comparing this data to signals. The speech patterns are stored on the hard drive and loaded into memory when the program is run. A comparator checks these stored patterns against the output of the A/D converter -- an action called pattern recognition .

In practice, the size of a voice recognition program's effective vocabulary is directly related to the RAM capacity of the computer in which it's installed. A voice recognition program runs many times faster if the entire vocabulary can be loaded into RAM compared to searching the hard drive for some of the matches. Processing speed is critical, as it affects how fast the computer can search the RAM for matches.

Audio also must be processed for clarity, so some devices may filter out background noise. In some voice recognition systems, certain frequencies in the audio are emphasized so the device can recognize a voice better.

Voice recognition systems analyze speech through one of two models: the hidden Markov model and neural networks. The hidden Markov model breaks down spoken words into their phonemes, while recurrent neural networks use the output from previous steps to influence the input to the current step.

As uses for voice recognition technology grow and more users interact with it, the organizations implementing voice recognition software will have more data and information to feed into neural networks for voice recognition systems. This improves the capabilities and accuracy of voice recognition products.

The popularity of smartphones opened up the opportunity to add voice recognition technology into consumer pockets, while home devices -- such as Google Home and Amazon Echo -- brought voice recognition technology into living rooms and kitchens.

Voice recognition uses

The uses for voice recognition have grown quickly as AI, machine learning and consumer acceptance have matured. Examples of how voice recognition is used include the following:

• Virtual assistants. Siri, Alexa and Google virtual assistants all implement voice recognition software to interact with users. The way consumers use voice recognition technology varies depending on the product. But they can use it to transcribe voice to text, set up reminders, search the internet and respond to simple questions and requests, such as play music or share weather or traffic information.
• Smart devices. Users can control their smart homes – including smart thermostats and smart speakers -- using voice recognition software.
• Automated phone systems. Organizations use voice recognition with their phone systems to direct callers to a corresponding department by saying a specific number.
• Conferencing. Voice recognition is used in live captioning a speaker so others can follow what is said in real time as text.
• Bluetooth. Bluetooth systems in modern cars support voice recognition to help drivers keep their eyes on the road. Drivers can use voice recognition to perform commands such as "call my office."
• Dictation and voice recognition software. These tools can help users dictate and transcribe documents without having to enter text using a physical keyboard or mouse.
• Government. The National Security Agency has used voice recognition systems dating back to 2006 to identify terrorists and spies or to verify the audio of anyone speaking.

Voice recognition advantages and disadvantages

Voice recognition offers numerous benefits:

• Consumers can multitask by speaking directly to their voice assistant or other voice recognition technology.
• Users who have trouble with sight can still interact with their devices.
• Machine learning and sophisticated algorithms help voice recognition technology quickly turn spoken words into written text.
• This technology can capture speech faster than some users can type. This makes tasks like taking notes or setting reminders faster and more convenient.

However, some disadvantages of the technology include the following:

• Background noise can produce false input.
• While accuracy rates are improving, all voice recognition systems and programs make errors.
• There's a problem with words that sound alike but are spelled differently and have different meanings -- for example, hear and here . This issue might be largely overcome using stored contextual information. However, this requires more RAM and faster processors .

For more on artificial intelligence in the enterprise, read the following articles:

What is an intelligent agent?

What is artificial general intelligence?

What is artificial superintelligence?

What is language modeling?

What is natural language processing?

History of voice recognition

Voice recognition technology has grown exponentially over the past five decades. Dating back to 1976, computers could only understand slightly more than 1,000 words. That total jumped to roughly 20,000 in the 1980s as IBM continued to develop voice recognition technology.

In 1952, Bell Laboratories invented AUDREY -- the Automatic Digit Recognizer -- which could only understand the numbers zero through nine. In the early to mid-1970s, the U.S. Department of Defense started contributing toward speech recognition system development, funding the Defense Advanced Research Projects Agency Speech Understanding Research. Harpy, developed by Carnegie Mellon, was another voice recognition system at the time and could recognize up to 1,011 words.

The company Dragon in 1990 launched the first speaker recognition product for consumers, Dragon Dictate. This was later replaced by Dragon NaturallySpeaking from Nuance Communications. In 1997, IBM introduced IBM ViaVoice, the first voice recognition product that could recognize continuous speech.

Apple introduced Siri in 2011, and it's still a prominent voice recognition assistant. In 2016, Google launched its Google Assistant for phones. Voice recognition systems can be found in devices including phones, smart speakers, laptops, desktops and tablets as well as in software like Dragon Professional and Philips SpeechLive.

During this past decade, several other technology leaders have developed more sophisticated voice recognition software, such as Amazon Alexa, for example. Released in 2014, Amazon Alexa also acts as a personal assistant that responds to voice commands. Currently, voice recognition software is available for Windows, Mac, Android, iOS and Windows phone devices.

Continue Reading About voice recognition (speaker recognition)

What's the difference between speech and voice recognition.

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Today, when we call most large companies, a person doesn't usually answer the phone. Instead, an automated voice recording answers and instructs you to press buttons to move through option menus. Many companies have moved beyond requiring you to press buttons, though. Often you can just speak certain words (again, as instructed by a recording) to get what you need. The system that makes this possible is a type of speech recognition program -- an automated phone system.

You an also use speech recognition software in homes and businesses. A range of software products allows users to dictate to their computer and have their words converted to text in a word processing or e-mail document. You can access function commands, such as opening files and accessing menus, with voice instructions. Some programs are for specific business settings, such as medical or legal transcription.

People with disabilities that prevent them from typing have also adopted speech-recognition systems. If a user has lost the use of his hands, or for visually impaired users when it is not possible or convenient to use a Braille keyboard, the systems allow personal expression through dictation as well as control of many computer tasks. Some programs save users' speech data after every session, allowing people with progressive speech deterioriation to continue to dictate to their computers.

Current programs fall into two categories:

Small-vocabulary/many-users

These systems are ideal for automated telephone answering. The users can speak with a great deal of variation in accent and speech patterns, and the system will still understand them most of the time. However, usage is limited to a small number of predetermined commands and inputs, such as basic menu options or numbers.

Large-vocabulary/limited-users

These systems work best in a business environment where a small number of users will work with the program. While these systems work with a good degree of accuracy (85 percent or higher with an expert user) and have vocabularies in the tens of thousands, you must train them to work best with a small number of primary users. The accuracy rate will fall drastically with any other user.

Speech recognition systems made more than 10 years ago also faced a choice between discrete and continuous speech. It is much easier for the program to understand words when we speak them separately, with a distinct pause between each one. However, most users prefer to speak in a normal, conversational speed. Almost all modern systems are capable of understanding continuous speech.

For this article, we spoke with John Garofolo , Speech Group Manager at the Information Technology Laboratory of the National Institute of Standards and Technology . We'd also like to thank Joshua Senecal for his assistance with this article .

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Voice Recognition

What is Voice Recognition and How Does it Work?

Voice recognition refers to the ability to identify speakers and transform spoken words into data and actions. This technology, powered by artificial intelligence (AI), has enabled machines to process human speech, bridging the gap between the two, and enabling new technologies to emerge. 

Today, voice technology has changed the way we interact with our devices, whether it’s a mobile phone, a watch, or even smart home devices. This technology lets us operate our devices hands-free while using natural language for commands. Because of this, voice recognition helps make technology more accessible to those with impairments or mobility limitations.

This blog post will give you more of a background on voice recognition, how it works, its use cases and capabilities, and a look at how it’s applied in businesses with platforms like aiOla.

How Does Voice Recognition Work?

Voice recognition uses various technologies to transform voice into text. Through various voice-to-text technologies, language goes through a conversion process that breaks audio down into phonetic components, which helps these systems recognize patterns and translate them into written words.

While often used interchangeably, speech recognition, or automatic speech recognition (ASR) , and voice recognition aren’t quite the same. ASR technology is a specific component within voice technology that’s focused on transcribing spoken words into text. By contrast, voice recognition is more focused on the wider range of abilities related to processing spoken language. 

Through the use of technologies like AI, deep learning, and machine learning (ML), voice systems can understand the language we use, including accents, slang, abbreviations, and dialects. After being trained on vast sets of language-based data, ML works to look at a pattern of speech and extract data using neural networks.

AI and ML enable voice technology systems to adapt continuously and improve their abilities to understand diverse linguistic variations and nuances. Today, these systems can already do so much more than simply recognize voices and languages and can complete actions like answering questions, responding to commands, and directing requests through voice alone.

Voice Recognition Examples and Applications

Many voice applications are already embedded in our society, such as voice search and smart devices. In 2024, there will be 8 billion digital voice assistants in use, making this a rapidly growing field. However, to truly understand the power of this technology, including its potential for future use, let’s first look at how it’s being used today.

Virtual Assistants

Voice recognition empowers voice assistants like Siri, Alexa, and Google Assistant. Whether you’re asking these assistants to check the weather or complete an action like setting a reminder, this process is done by turning your voice into text. This technology allows us to engage with these AI-driven assistants through natural language.

Voice-activated Devices

Smart devices like smart speakers, TVs, watches, and mobiles allow us to use technology entirely hands-free. Users can control and navigate these gadgets, conduct a search, order items, and adjust settings with their voice.

Accessibility For Individuals with Disabilities

Voice recognition breaks barriers for individuals with disabilities, such as visual impairments, to access tools that the rest of society uses by operating it in a different way, such as through voice instead of touch. With voice control over devices, technology becomes more accessible for those with mobility challenges, making the digital experience more inclusive.

Voice Biometrics for Security

By measuring the unique vocal characteristics of an individual’s voices, we can use voice recognition technology as an additional way to secure authentication. For example, when verifying identity when calling customer service, it adds an extra layer of personalized security.

The Advantages and Challenges of Voice Recognition Software

While voice technology comes with notable advantages, the private or commercial use of this technology presents a unique set of challenges. Below, we’ll examine both the pros and cons of voice recognition software and systems to point out its strengths and the areas where it can still be improved.

• Hands-free operation allows users to interact with devices without physically handling it
• Increased accessibility makes a more inclusive and accessible digital experience
• Makes daily tasks like setting reminders, searching the internet, or sending messages more efficient
• Enhances productivity in personal and professional settings by making tasks quick and convenient to execute
• Accuracy issues can arise with variability in accents, speech patterns, and background noise
• Storing voice data leads to ethical and privacy concerns such as misuse or unauthorized access
• Background and ambient noise can impact a system’s accuracy and ability to process commands
• Many voice recognition systems are cloud-based and depend on an internet connection, meaning functionality is impacted when there’s a lack of a stable connection

Voice Recognition in Business

While we’ve seen how voice recognition tools are used in our day-to-day lives, it also has many applications in the business landscape. By turning manual tasks into hands-free operations, it has the power to change operations and make workflows more efficient. Here’s a look at just some of the ways this technology is being used in the workplace.

• Customer support: Automated and interactive voice recognition (IVR) systems help businesses route client calls and personalize interactions for a more engaging and pleasant experience, with the market for this technology expected to almost double by 2030
• Transcription: Many fields rely on transcription for easier documentation, such as healthcare, journalism, education, or for tracking discussions in all types of meetings
• Inventory management: In warehouses and logistics teams, voice systems help employees manage inventory and orders more accurately and efficiently.
• Automotive: Many new cars are equipped with voice recognition software for hands-free operation of temperature, radio, and navigation, leading to a safer driving experience.
• Multilingual communication: When paired with other tools, voice recognition can detect and then translate language in real time, breaking down language barriers in professional settings.

Bringing Voice Technology to More Industries with aiOla

With our cutting-edge AI-powered speech platform, aiOla is bringing voice-driven automation to industries like fleet management, food safety, manufacturing, and others. aiOla’s platform collects data through speech, which otherwise would not have been collected, to complete mission-critical tasks such as inspections and equipment maintenance predictions. Without aiOla, teams need to spend more time on these manual tasks and still run the risk of making mistakes or getting less accurate results.

aiOla understands over 100 languages as well as various dialects, accents, and industry jargon, making it simple for our platform to pick up on important speech so that companies can use the gathered data to make important business decisions. aiOla is helping these industries put tasks on autopilot simply by using the power of language, without the need for a cumbersome onboarding process. Here’s a look at how aiOla’s voice recognition technology makes a difference:

• Food manufacturing companies are increasing production time by 30% by getting real-time insights on machinery maintenance, automating digital workflows, and cutting down on time spent on inspections
• In the fleet management industry , aiOla’s voice platform is enabling hands-free vehicle operation while using voice technology to quickly inspect vehicles more accurately, resulting in an 85% time savings
• Warehouse and logistics teams using aiOla have been able to operate more safely by sharing updates through speech, leading to a 25% increase in the number of pallets handled per hour while simultaneously decreasing safety issues per shift

Harnessing the Power of Voice Recognition

Voice recognition software has come a long way. By making our personal and professional lives more safe and efficient, there’s no doubt that this technology will continue to develop and expand to other areas of our lives and more industries. aiOla is at the forefront of using voice technology to improve operations in essential services, helping teams gather important data and work more securely.

Book a demo with one of our experts to see how aiOla’s voice technology works in action.

How accurate is voice recognition technology?

The accuracy of voice recognition technology varies but has drastically improved over recent years. Still, there are challenges when incorporating diverse languages and accents, as well as background noises. Still, some technologies do a better job of overcoming these roadblocks to deliver a highly accurate result.

What are the privacy concerns associated with voice recognition?

Privacy concerns related to voice recognition include the misuse of voice data and malicious actors gaining unauthorized access to sensitive recordings.

Can voice recognition be used by individuals with speech disabilities?

Yes, voice recognition systems are ideal for individuals with speech disabilities as they can learn new speech patterns and offer an accessible and efficient means of communication.

How does ambient noise affect the performance of voice recognition systems?

Ambient noise has the potential to negatively affect the performance of a voice recognition system by interfering with audio input, leading to a misinterpretation of voice data.

Are there any ethical considerations related to voice recognition technology?

There are ethical considerations involved in using voice recognition technology, such as consent, security and privacy concerns, as well as bias in AI algorithms.

What are the potential future developments in voice recognition?

In the future, we expect the development of voice recognition tools to enhance natural language processing, improve in accuracy, and integrate more organically with AI, ML, and other technologies. Interactions will likely become more personalized as voice technology gets better at understanding context and individual speech patterns.

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Home  >  Lexacom Insight  >  How Does Speech Recognition Work?

How Does Speech Recognition Work? 

Speech recognition software has come a long way since its first inception in the 1950s. Back then, this technology could only understand up to 16 words, including the digits 0 to 9. 

Now, we use speech recognition technology in our everyday lives, with an increasing amount of people using assistants like Google Home, Siri, and Amazon Alexa.

But, what exactly is speech recognition software?

Speech recognition software is a form of technology that is capable of processing human speech, interpreting it, and transcribing it into written text. 

This technology is not only used in our everyday lives but it is also key to improving productivity in our busy workplaces. 

From the healthcare industry to the legal sector, speech recognition is a crucial part of streamlining admin processes. 

This solution improves efficiency by freeing users from their keyboards and saving them precious time that can be used on more demanding tasks.

So, how does speech recognition work?

At its core, speech recognition software works by breaking down a speech recording into individual sounds. 

This technology then analyses each sound and uses an algorithm to find the most probable word fit for that sound. Finally, those sounds are transcribed into text.

The process of speech recognition can be broken down into 3 stages:

• Automatic speech recognition (ASR)
• Natural language processing (NLP)
• Speech-to-Text (STS)

Automatic Speech Recognition

Automatic speech recognition (ASR) corresponds to the process of digitising a recorded speech sample. The speaker’s voice template is broken up into small segments of tones that can be visualised in the form of spectrograms .

Natural Language Processing

The next step in the speech recognition process is to use a natural language processing (NLP) algorithm to analyse and transcribe each individual spectrogram.

AI-based natural language algorithms predict the probability of all words in a language’s vocabulary. A contextual layer is added to help correct any potential mistakes.

This stage is extremely important. Because if the speech recognition software that you’re using doesn’t have an appropriate dictionary for your profession, it is more likely to result in errors in recognising industry-specific words.

Lexacom Echo has revolutionised the professional speech recognition market by providing users with professional-grade natural language technology that supports profession-specific dictionaries.

The profession-specific vocabularies for medical, legal, and business are fully integrated and updated regularly ensuring consistent accuracy.

Speech-to-Text

Once natural processing occurs the speech is fully transcribed to the display screen via the speech-to-text (STT) capability of the software 

Lexacom’s Speech Recognition Solution 

At Lexacom, we’ve harnessed the power of speech recognition technology with Lexacom Echo – a world-leading, AI-powered, professional-grade speech recognition system. 

Lexacom Echo doesn’t need any voice training. You simply place your cursor on the document where you would type, and speak. 

Lexacom Echo can process speech at a speed of 160 words per minute. Given that’s almost three times faster than typing, you’d struggle to find a reason not to want to use Lexacom Echo in your team. 

With its easy-to-use interface and precision, Lexacom Echo guarantees a high level of accuracy at all times. We secure this by ensuring our software is familiar with specific professional terminology.

Let Lexacom take care of your speech recognition needs

If you want to speak to one of our experts about demoing our speech recognition software, Lexacom Echo, or having a free product trial, s imply fill out our contact form , and we will be in touch. 

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Voice Recognition System – Types, How it Works, Architecture, Applications

Voice Recognition System is something that has been dreamt about and worked on for decades. It has become a popular concept from past few years. From individuals to organizations, this technology is broadly used for various advantages it provides. In this post we will discuss about what is Voice Recognition System, how it works, it’s types, architecture, applications, advantages and disadvantages.

What is Voice Recognition System

Voice Recognition Technology is basically the task of identifying what is being uttered by a speaker in text form. The utterance can be an isolated word or sentence or may even be a paragraph. The algorithm implemented as a computer program converts a speech signal to a sequence of words.

Fig. 1 – Introduction to Voice Recognition system

Digital Assistants such as Amazon’s Alexa, Google’s Google Assistant, Apple’s Siri and Microsoft’s Cortana are making a huge difference in daily life by changing the way people interact with their devices, homes, cars, and jobs. These technologies allow us to interact to a computer or device that interprets what we’re saying and respond to our question or command.

Fig.2 shows typical block diagram of Voice Recognition System where the input speech undergoes Acoustic Modeling where the speech is transformed in to statistical representations of Vectors which is computed from Voice signal. Then the speech (Word or Sentence) is searched and matched with the data in the system and outputs the Recognized Utterance.

Fig. 2 – Typical Block Diagram of Speech (Voice) Recognition System

• Types of Voice Recognition System

They are of two types:

• Text Dependent Voice Recognition System

Text Independent Voice Recognition System

These systems require the speaker to say a predetermined word or phrase (known as “Pass Phrase”). This Pass Phrase is then compared to an already captured sample.

These systems are trained to recognize a person without a Pass Phrase. But they require longer speech inputs from the speaker in order to identify vocal characteristics.

• Architecture of Voice Recognition System

The architecture of the system consists of following modules:

• Speech Capturing Device
• Digital Signal Processor Module

Pre-processed Signal Storage

Reference speech patterns, pattern matching algorithm.

Fig. 3 – Architecture of Voice Recognition System

Speech Capturing Device is a microphone that converts sound waves into electrical signals and an Analog to Digital Converter (ADC) that digitizes the analog signals to obtain the data, that the computer can understand.

• Digital Signal Module

This module performs processing on the raw speech signal like frequency domain conversion, restoring only the required information etc.

This storage stores pre-processed Voice.

The system consists of predefined Voice sample which is used as a Reference for matching.

The unknown speech signal is compared with the Reference Speech Pattern to find the actual words or the pattern of words.

How does Voice Recognition System Work

This System works by recording a voice sample of a person’s speech through Speech Capture Device like Microphone. The Voice is nothing but analog signal is passed through noisy communication channel. Analog to Digital Converter (ADC) converts the analog signal into digital data by Sampling and Digitization process.

Then the system filters the unwanted noise and divides it into different frequency bands and normalizes the sound. This is done as the users do not always speak at the same speed and volume. Hence sound has to be adjusted to match with the templates that are pre-stored in the database of the system.

Fig. 4 – Working of Speech (Voice) Recognition System

For large vocabulary Speech Recognition like long Sentences,  is decomposed into sub-word sequence. This process is called Segmentation. This process is carried out on the signal where the signal is divided into segments and further processed by the Signal-Processing module that extracts Feature Vectors. These extracted Vectors form the input to the Decoder.

Acoustic Model, Pronunciation Model and Language Models are used by the Decoder to generate the word sequence which matches with the input Feature Vectors. Voice Recognition System use statistical modeling systems which use probability and mathematical functions to determine the most likely outcome.

The Speech Decoder decodes the acoustic signal X into a word sequence W*, which is close to the original word sequence W. It is represented by the equation of statistical Speech Recognition given by:

Applications of Voice Recognition System

The applications of Voice Recognition Technology include:

Applications of Speech Recognition System in the workplace include:

• Search for documents or reports on your computer
• Create tables or graphs using data
• Print documents on request
• Start video conferences
• Schedule meetings
• Make travel arrangements

Applications of Speech Recognition system in banking include:

• Fetch information regarding your transactions, balance without having to open your cell phone
• Make payments
• Receive information about your transaction history

Voice System has the potential to add a new way marketers reach their consumers. With speech recognition, there will be a new type of data available for marketers to analyze.

Applications of Speech Recognition System in healthcare include:

• Quickly finding information from medical records
• Workers can be reminded of instructions or processes
• One can ask queries related to an disease from home
• Less time inputting data
• Improved workflows

Internet of Things (IoT)

One of the most important applications of Voice Recognition System in the internet of things is in cars. Examples of digital assistants applications in car are:

• Listen to messages hands-free
• Control your Radio
• Assist with guidance and navigation
• Respond to voice commands

Advantages of Voice Recognition System

The advantages of Voice Recognition Technology include:

• Speech Recognition Technology is helping people by allowing people with disabilities to type and operate computers.
• It is easy and fast.
• This System is easy to use over the phone or other speaking devices and thus it is useful.
• Speech Recognition System is quite reasonable.
• Accidents while driving due to texting is very common. With Speech Recognition Technology, people will be able to write text and create email without diverting their eyes from road. Hence Automobile Safety is assured.

Disadvantages of Voice Recognition System

The disadvantages of Voice Recognition Technology include:

• Lack of Accuracy and Misinterpretation- While Voice Recognition Technology recognizes most words in English language, it still struggles to recognize names and slang words. It also cannot differentiate between homophones such as “their” and “there”.
• Time Costs and Productivity- No doubt technology can speed up process but in case of Voice Recognition System, user may have to invest more time than expected. Users have to review and edit to correct errors. Some programs adapt to your voice and speech patterns over time; this may slow down your workflow until the program is up to speed. You’ll also have to learn how to use the system.
• analog signals
• Analog to Digital Converter
• Applications
• Communication Channel
• disadvantages
• electrical signals
• equation of statistical speech recognition
• Feature Vectors
• pass phrase
• Pattern matching algorithm
• Preprocessed signal storage
• probability of Acoustic Model
• probability of Language Model
• probability of Pronunciation Model
• Reference Speech patterns
• Segmentation
• sound waves
• statistical modeling system
• Text independent Voice Recognition System
• Voice Recognition System

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2024 Theses Doctoral

Spectro-Temporal and Linguistic Processing of Speech in Artificial and Biological Neural Networks

Keshishian, Menoua

Humans possess the fascinating ability to communicate the most complex of ideas through spoken language, without requiring any external tools. This process has two sides—a speaker producing speech, and a listener comprehending it. While the two actions are intertwined in many ways, they entail differential activation of neural circuits in the brains of the speaker and the listener. Both processes are the active subject of artificial intelligence research, under the names of speech synthesis and automatic speech recognition, respectively. While the capabilities of these artificial models are approaching human levels, there are still many unanswered questions about how our brains do this task effortlessly. But the advances in these artificial models allow us the opportunity to study human speech recognition through a computational lens that we did not have before. This dissertation explores the intricate processes of speech perception and comprehension by drawing parallels between artificial and biological neural networks, through the use of computational frameworks that attempt to model either the brain circuits involved in speech recognition, or the process of speech recognition itself. There are two general types of analyses in this dissertation. The first type involves studying neural responses recorded directly through invasive electrophysiology from human participants listening to speech excerpts. The second type involves analyzing artificial neural networks trained to perform the same task of speech recognition, as a potential model for our brains. The first study introduces a novel framework leveraging deep neural networks (DNNs) for interpretable modeling of nonlinear sensory receptive fields, offering an enhanced understanding of auditory neural responses in humans. This approach not only predicts auditory neural responses with increased accuracy but also deciphers distinct nonlinear encoding properties, revealing new insights into the computational principles underlying sensory processing in the auditory cortex. The second study delves into the dynamics of temporal processing of speech in automatic speech recognition networks, elucidating how these systems learn to integrate information across various timescales, mirroring certain aspects of biological temporal processing. The third study presents a rigorous examination of the neural encoding of linguistic information of speech in the auditory cortex during speech comprehension. By analyzing neural responses to natural speech, we identify explicit, distributed neural encoding across multiple levels of linguistic processing, from phonetic features to semantic meaning. This multilevel linguistic analysis contributes to our understanding of the hierarchical and distributed nature of speech processing in the human brain. The final chapter of this dissertation compares linguistic encoding between an automatic speech recognition system and the human brain, elucidating their computational and representational similarities and differences. This comparison underscores the nuanced understanding of how linguistic information is processed and encoded across different systems, offering insights into both biological perception and artificial intelligence mechanisms in speech processing. Through this comprehensive examination, the dissertation advances our understanding of the computational and representational foundations of speech perception, demonstrating the potential of interdisciplinary approaches that bridge neuroscience and artificial intelligence to uncover the underlying mechanisms of speech processing in both artificial and biological systems.

• Neurosciences
• Artificial intelligence
• Neural networks (Neurobiology)
• Neural networks (Computer science)
• Automatic speech recognition

More About This Work

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What Kamala Harris has said so far on key issues in her campaign

As she ramps up her nascent presidential campaign, Vice President Kamala Harris is revealing how she will address the key issues facing the nation.

In speeches and rallies, she has voiced support for continuing many of President Joe Biden’s measures, such as lowering drug costs , forgiving student loan debt and eliminating so-called junk fees. But Harris has made it clear that she has her own views on some key matters, particularly Israel’s treatment of Gazans in its war with Hamas.

In a departure from her presidential run in 2020, the Harris campaign has confirmed that she’s moved away from many of her more progressive stances, such as her interest in a single-payer health insurance system and a ban on fracking.

Harris is also expected to put her own stamp and style on matters ranging from abortion to the economy to immigration, as she aims to walk a fine line of taking credit for the administration’s accomplishments while not being jointly blamed by voters for its shortcomings.

Her early presidential campaign speeches have offered insights into her priorities, though she’s mainly voiced general talking points and has yet to release more nuanced plans. Like Biden, she intends to contrast her vision for America with that of former President Donald Trump. ( See Trump’s campaign promises here .)

“In this moment, I believe we face a choice between two different visions for our nation: one focused on the future, the other focused on the past,” she told members of the historically Black sorority Zeta Phi Beta at an event in Indianapolis in late July. “And with your support, I am fighting for our nation’s future.”

Here’s what we know about Harris’ views:

Harris took on the lead role of championing abortion rights for the administration after Roe v. Wade was overturned in June 2022. This past January, she started a “ reproductive freedoms tour ” to multiple states, including a stop in Minnesota thought to be the first by a sitting US president or vice president at an abortion clinic .

On abortion access, Harris embraced more progressive policies than Biden in the 2020 campaign, as a candidate criticizing his previous support for the Hyde Amendment , a measure that blocks federal funds from being used for most abortions.

Policy experts suggested that although Harris’ current policies on abortion and reproductive rights may not differ significantly from Biden’s, as a result of her national tour and her own focus on maternal health , she may be a stronger messenger.

High prices are a top concern for many Americans who are struggling to afford the cost of living after a spell of steep inflation. Many voters give Biden poor marks for his handling of the economy, and Harris may also face their wrath.

In her early campaign speeches, Harris has echoed many of the same themes as Biden, saying she wants to give Americans more opportunities to get ahead. She’s particularly concerned about making care – health care, child care, elder care and family leave – more affordable and available.

Harris promised at a late July rally to continue the Biden administration’s drive to eliminate so-called “junk fees” and to fully disclose all charges, such as for events, lodging and car rentals. In early August, the administration proposed a rule that would ban airlines from charging parents extra fees to have their kids sit next to them.

On day one, I will take on price gouging and bring down costs. We will ban more of those hidden fees and surprise late charges that banks and other companies use to pad their profits.”

Since becoming vice president, Harris has taken more moderate positions, but a look at her 2020 campaign promises reveals a more progressive bent than Biden.

As a senator and 2020 presidential candidate, Harris proposed providing middle-class and working families with a refundable tax credit of up to $6,000 a year (per couple) to help keep up with living expenses. Titled the LIFT the Middle Class Act, or Livable Incomes for Families Today, the measure would have cost at the time an estimated $3 trillion over 10 years.

Unlike a typical tax credit, the bill would allow taxpayers to receive the benefit – up to $500 – on a monthly basis so families don’t have to turn to payday loans with very high interest rates.

As a presidential candidate, Harris also advocated for raising the corporate income tax rate to 35%, where it was before the 2017 Tax Cuts and Jobs Act that Trump and congressional Republicans pushed through Congress reduced the rate to 21%. That’s higher than the 28% Biden has proposed.

Affordable housing was also on Harris’ radar. As a senator, she introduced the Rent Relief Act, which would establish a refundable tax credit for renters who annually spend more than 30% of their gross income on rent and utilities. The amount of the credit would range from 25% to 100% of the excess rent, depending on the renter’s income.

Harris called housing a human right and said in a 2019 news release on the bill that every American deserves to have basic security and dignity in their own home.

Consumer debt

Hefty debt loads, which weigh on people’s finances and hurt their ability to buy homes, get car loans or start small businesses, are also an area of interest to Harris.

As vice president, she has promoted the Biden administration’s initiatives on student debt, which have so far forgiven more than $168 billion for nearly 4.8 million borrowers . In mid-July, Harris said in a post on X that “nearly 950,000 public servants have benefitted” from student debt forgiveness, compared with only 7,000 when Biden was inaugurated.

A potential Harris administration could keep that momentum going – though some of Biden’s efforts have gotten tangled up in litigation, such as a program aimed at cutting monthly student loan payments for roughly 3 million borrowers enrolled in a repayment plan the administration implemented last year.

The vice president has also been a leader in the White House efforts to ban medical debt from credit reports, noting that those with medical debt are no less likely to repay a loan than those who don’t have unpaid medical bills.

In a late July statement praising North Carolina’s move to relieve the medical debt of about 2 million residents, Harris said that she is “committed to continuing to relieve the burden of medical debt and creating a future where every person has the opportunity to build wealth and thrive.”

Health care

Harris, who has had shifting stances on health care in the past, confirmed in late July through her campaign that she no longer supports a single-payer health care system .

During her 2020 campaign, Harris advocated for shifting the US to a government-backed health insurance system but stopped short of wanting to completely eliminate private insurance.

The measure called for transitioning to a Medicare-for-All-type system over 10 years but continuing to allow private insurance companies to offer Medicare plans.

The proposal would not have raised taxes on the middle class to pay for the coverage expansion. Instead, it would raise the needed funds by taxing Wall Street trades and transactions and changing the taxation of offshore corporate income.

When it comes to reducing drug costs, Harris previously proposed allowing the federal government to set “a fair price” for any drug sold at a cheaper price in any economically comparable country, including Canada, the United Kingdom, France, Japan or Australia. If manufacturers were found to be price gouging, the government could import their drugs from abroad or, in egregious cases, use its existing but never-used “march-in” authority to license a drug company’s patent to a rival that would produce the medication at a lower cost.

Harris has been a champion on climate and environmental justice for decades. As California’s attorney general, Harris sued big oil companies like BP and ConocoPhillips, and investigated Exxon Mobil for its role in climate change disinformation. While in the Senate, she sponsored the Green New Deal resolution.

During her 2020 campaign, she enthusiastically supported a ban on fracking — but a Harris campaign official said in late July that she no longer supports such a ban.

Fracking is the process of using liquid to free natural gas from rock formations – and the primary mode for extracting gas for energy in battleground Pennsylvania. During a September 2019 climate crisis town hall hosted by CNN, she said she would start “with what we can do on Day 1 around public lands.” She walked that back later when she became Biden’s running mate.

Biden has been the most pro-climate president in history, and climate advocates find Harris to be an exciting candidate in her own right. Democrats and climate activists are planning to campaign on the stark contrasts between Harris and Trump , who vowed to push America decisively back to fossil fuels, promising to unwind Biden’s climate and clean energy legacy and pull America out of its global climate commitments.

If elected, one of the biggest climate goals Harris would have to craft early in her administration is how much the US would reduce its climate pollution by 2035 – a requirement of the Paris climate agreement .

Immigration

Harris has quickly started trying to counter Trump’s attacks on her immigration record.

Her campaign released a video in late July citing Harris’ support for increasing the number of Border Patrol agents and Trump’s successful push to scuttle a bipartisan immigration deal that included some of the toughest border security measures in recent memory.

The vice president has changed her position on border control since her 2020 campaign, when she suggested that Democrats needed to “critically examine” the role of Immigration and Customs Enforcement, or ICE, after being asked whether she sided with those in the party arguing to abolish the department.

In June of this year, the White House announced a crackdown on asylum claims meant to continue reducing crossings at the US-Mexico border – a policy that Harris’ campaign manager, Julie Chavez Rodriguez, indicated in late July to CBS News would continue under a Harris administration.

Trump’s attacks stem from Biden having tasked Harris with overseeing diplomatic efforts in Central America in March 2021. While Harris focused on long-term fixes, the Department of Homeland Security remained responsible for overseeing border security.

She has only occasionally talked about her efforts as the situation along the US-Mexico border became a political vulnerability for Biden. But she put her own stamp on the administration’s efforts, engaging the private sector.

Harris pulled together the Partnership for Central America, which has acted as a liaison between companies and the US government. Her team and the partnership are closely coordinating on initiatives that have led to job creation in the region. Harris has also engaged directly with foreign leaders in the region.

Experts credit Harris’ ability to secure private-sector investments as her most visible action in the region to date but have cautioned about the long-term durability of those investments.

Israel-Hamas

The Israel-Hamas war is the most fraught foreign policy issue facing the country and has spurred a multitude of protests around the US since it began in October.

After meeting with Israeli Prime Minister Benjamin Netanyahu in late July, Harris gave a forceful and notable speech about the situation in Gaza.

We cannot look away in the face of these tragedies. We cannot allow ourselves to become numb to the suffering. And I will not be silent.”

Harris echoed Biden’s repeated comments about the “ironclad support” and “unwavering commitment” to Israel. The country has a right to defend itself, she said, while noting, “how it does so, matters.”

However, the empathy she expressed regarding the Palestinian plight and suffering was far more forceful than what Biden has said on the matter in recent months. Harris mentioned twice the “serious concern” she expressed to Netanyahu about the civilian deaths in Gaza, the humanitarian situation and destruction she called “catastrophic” and “devastating.”

She went on to describe “the images of dead children and desperate hungry people fleeing for safety, sometimes displaced for the second, third or fourth time.”

Harris emphasized the need to get the Israeli hostages back from Hamas captivity, naming the eight Israeli-American hostages – three of whom have been killed.

But when describing the ceasefire deal in the works, she didn’t highlight the hostage for prisoner exchange or aid to be let into Gaza. Instead, she singled out the fact that the deal stipulates the withdrawal by the Israeli military from populated areas in the first phase before withdrawing “entirely” from Gaza before “a permanent end to the hostilities.”

Harris didn’t preside over Netanyahu’s speech to Congress in late July, instead choosing to stick with a prescheduled trip to a sorority event in Indiana.

Harris is committed to supporting Ukraine in its fight against Russian aggression, having met with Ukrainian President Volodymyr Zelensky at least six times and announcing last month $1.5 billion for energy assistance, humanitarian needs and other aid for the war-torn country.

At the Munich Security Conference earlier this year, Harris said: “I will make clear President Joe Biden and I stand with Ukraine. In partnership with supportive, bipartisan majorities in both houses of the United States Congress, we will work to secure critical weapons and resources that Ukraine so badly needs. And let me be clear: The failure to do so would be a gift to Vladimir Putin.”

More broadly, NATO is central to our approach to global security. For President Biden and me, our sacred commitment to NATO remains ironclad. And I do believe, as I have said before, NATO is the greatest military alliance the world has ever known.”

Police funding

The Harris campaign has also walked back the “defund the police” sentiment that Harris voiced in 2020. What she meant is she supports being “tough and smart on crime,” Mitch Landrieu, national co-chair for the Harris campaign and former mayor of New Orleans, told CNN’s Pamela Brown in late July.

In the midst of nationwide 2020 protests sparked by George Floyd’s murder by a Minneapolis police officer, Harris voiced support for the “defund the police” movement, which argues for redirecting funds from law enforcement to social services. Throughout that summer, Harris supported the movement and called for demilitarizing police departments.

Democrats largely backed away from calls to defund the police after Republicans attempted to tie the movement to increases in crime during the 2022 midterm elections.

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How the US Medical System Failed this Pokemon Voice Actor

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• Rachael Lillis, a renowned voice actress, tragically lost her battle with cancer at 55.
• American medical system failures compounded her struggle, leading to her untimely passing.
• Despite the heartbreaking outcome, the overwhelming support from fans helped bring comfort.

On August 10, 2024, Rachael Lillis - an acclaimed voice actor for many years - died at the age of 55 after losing a battle with cancer. For fans of her work, the loss has been a difficult one, with many of them (along with co-workers) posting tributes and reminiscing about her most popular work on social media. While fans process her loss, for months Rachael was fighting two battles: cancer and the US medical system. In a way, she would lose to both, and the story is as tragic as it is frustrating. These were the final days of this acclaimed Pokemon voice actress.

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Who was Rachael Lillis?

Rachael Lillis was an American voice actress best known for her work in various anime series. Born in Niagara Falls, New York on July 8, 1969, Rachael attended Smith College where she was a premed student. She studied voice acting in Boston and would move to New York City in 1996, appearing in various theater productions, animated series, and independent movies. Later in life she would also work as a scriptwriter and translator for various projects.

What Were Some of Her Notable Roles?

Rachael Lillis is known for several notable roles in animation. Some of her most prominent roles include:

• Pokémon : Rachael's most famous work would be in Pokemon , where she voiced Ash's traveling companion Misty, Team Rocket member Jessie, as well as various Pokemon like Jigglypuff, Goldeen, and Poliwag (she also voiced Pikachu in early episodes when the Japanese audio track couldn't be saved).
• My Little Pony : Rachael would voice the younger versions of Applejack and Granny Smith in flashbacks of My Little Pony .
• Slayers: Rachael would voice Hilda in the various Slayers anime productions.

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The Start of Her Nightmare

In May 2024, Rachael's sister Laurie Orr created a GoFundMe, announcing that Rachael had developed breast cancer at an earlier date and needed help getting the proper care she needed. While the need to open a GoFundMe page is a sensitive topic in itself , behind the scenes the voice of Misty and Jessie was suffering her own personal hell at the mercy of something everyday American's have to fight every day: the insurance companies.

For most Americans, insurance will dictate what doctors are available, what patients can be accepted for priority care, and what is available to the patient in terms of treatment options. Rachael found herself in such a situation, where she was the victim of being a sick person that didn't have the right kind of insurance.

The One Flew Over the Cokuu's Nest Hospital

While most would agree that hospitals are generally not a fun place to have to be, some hospitals will strike the fear of God into even the most dedicated atheist due to how poor the conditions of the building are. This is what happened with Rachael, who found herself in such a hospital. As her sister pointed out:

By the way, the nursing home was akin to scenes from the movie, “One Flew Over the Cuckoo’s Nest”!! My sister and I thought it was truly horrible, in spite of the nurses there, doing the best they could to care for too many. The yelling from down the hall, the noisy roommate…how could anyone possibly heal, or rest in that environment?!

This is a horrifying revelation to discover that the voice actor for some of our most cherished childhood memories was ever in a situation like that, but that does beg the question of how she wound up in such a place in the first place (and what it took to get her out of it).

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Insurance, Insurance, Insurance...

Rachael wound up in (what her sister described) the 'Cuckoo's Nest Hospital' as a result of her insurance putting her under the care of a very specific doctor when she was admitted to the hospital to deal with her medical conditions. During her stay, Rachael was placed in a nursing home because she was barely ambulatory enough to care for herself. Not only did this placement not do what it was intended to (the horrible living conditions likely contributed to this), but at one point her doctor abruptly discontinued her treatment without explanation.

Now, Rachael was in a situation where she was alone, unsure about what to do when it came to treating this horrible disease, and her family was far away (though she did receive a visit from a friend who she met during her anime career ). It wasn't until later that family members went to see her and explore any arrangements that could be made. The main goal with these arrangements was to place her in a healthier environment, where she could have proper medical oversight, the medications she would need, and healthier food that would contribute to the recovery process (the conditions at the previous facility were so bad the family considered filing a medical malpractice suit). The question was how were they to pay for all of this? Contrary to popular belief, most voice actors are not rich. They are much like independent contractors: going from one job to the next in a very competitive field.

The insurance wasn't going to help, but being a voice actress, Rachael did have one thing many Americans did not: fans . It was with this knowledge that on Mother's Day, Laurie Orr started the GoFundMe campaign. She had never done anything like this before, so navigating the app turned out to be tricky. Once she launched the campaign, word spread very quickly about it. Fellow Pokemon actors Eric Stuart and Veronica Taylor were a couple of the first to share the campaign, with news sites like Anime News Network and Comic Book Resources also reporting on it.

The GoFundMe Campaign

In a very short amount of time, the campaign had raised over $67,000, and plans were made to not only get her better care, but also to get her into rehab to help regain muscle that was lost while she was in the original nursing facility due to the neglect she experienced. Sadly, this story would not have a happy ending, for shortly after moving out of the facility, Rachael would take an unexpected turn for the worse and pass away. Her sister was not there for her and the entire family was heartbroken, and announced that the funds would be used for a memorial (with the remaining funds to be donated to cancer research).

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Fans Bring Joy in the Final Days

Despite how it all ended, one bright spot was that Pokemon fans - the ones who grew up with Rachael's wonderful work - stepped up to the plate to help her. They would ultimately donate more than $100,000 dollars for her to get the care she needed, and the outpouring of support was more than what anyone had expected. While Rachael did not make any public statements, she told her sister Laurie that she felt like George Bailey in It's A Wonderful Life , the classic Frank Capra classic in which a man gets a glimpse of what life would be like without him there, and the memorable ending in which the town bands together to help him out in his time of need.

The kindness of the fans can not be overstated in this story. While Rachael may have lost the battle too soon, the fans reminded her that what she did meant something to so many people, and these people turned out in droves when she needed help for herself. Rachael would also have tribute paid to her by many of the major news and media organizations (though who knows how she'd feel about that, as she always felt that voice actors should be heard and not seen). While it is hopeful that this situation spurs discussions on how voice actors are paid and our broken medical system, the happy ending is that the fans stepped up when our institutions didn't and Rachael's wonderful work will live on forever .

Source: Personal Correspondence with Laurie Orr

Pokémon (1997)