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країнська اردو tiếng việt walon 粵語 中文 edit links article talk english read edit view history tools tools move to sidebar hide actions read edit view history general what links here related changes upload file permanent link page information cite this page get shortened url switch to legacy parser print export download as pdf printable version in other projects wikimedia commons wikidata item appearance move to sidebar hide from wikipedia the free encyclopedia automatic conversion of spoken language into text for the human linguistic concept see speech perception this article has multiple issues please help improve it or discuss these issues on the talk page learn how and when to remove these messages this article needs more citations please help improve this article by adding citations to reliable sources unsourced material may be challenged and removed find sources speech recognition news newspapers books scholar jstor july 2025 learn how and when to remove this message this article may be too technical for most readers to understand please help improve it to make it understandable to non experts without removing the technical details july 2025 learn how and when to remove this message learn how and when to remove this message speech recognition automatic speech recognition asr computer speech recognition or speech to text stt is a sub field of computational linguistics concerned with methods and technologies that translate spoken language into text or other interpretable forms 1 speech recognition applications include voice user interfaces where the user speaks to a device which listens and processes the audio common voice applications include interpreting commands for calling call routing home automation and aircraft control these applications are called direct voice input productivity applications include searching audio recordings creating transcripts and dictation speech recognition can be used to analyse speaker characteristics such as identifying native language using pronunciation assessment 2 voice recognition 3 4 5 speaker identification 6 7 8 refers to identifying the speaker rather than speech contents recognizing the speaker can simplify the task of translating speech in systems trained on a specific person s voice it can also be used to authenticate the speaker as part of a security process history edit applications for speech recognition developed over many decades with progress accelerated due to advances in deep learning and the use of big data 9 these advances are reflected in an increase in academic papers 10 and greater system adoption 11 key areas of growth include vocabulary size more accurate recognition for unfamiliar speakers speaker independence and faster processing speed pre 1970 edit 1952 bell labs researchers stephen balashek 12 r biddulph and k h davis built audrey 13 for single speaker digit recognition their system located the formants in the power spectrum of each utterance 14 1960 gunnar fant developed and published the source filter model of speech production 15 1962 ibm s 16 word shoebox machine s speech recognition debuted at the 1962 world s fair 16 1966 linear predictive coding a speech coding method was proposed by fumitada itakura of nagoya university and shuzo saito of nippon telegraph and telephone 17 1969 funding at bell labs came to a halt for several years after the company s head engineer john r pierce wrote an open letter criticizing speech recognition research 18 this defunding lasted until pierce retired and james l flanagan took over raj reddy was the first person to work on continuous speech recognition 19 as a graduate student at stanford university in the late 1960s previous systems required users to pause after each word reddy s system issued spoken commands for playing chess around this time soviet researchers invented the dynamic time warping dtw algorithm 20 and used it to create a recognizer capable of operating on a 200 word vocabulary 21 dtw processed speech by dividing it into short frames e g 10 ms segments and treating each frame as a unit speaker independence however remained unsolved 1970 1990 edit 1971 darpa funded a five year speech recognition research project speech understanding research seeking a minimum vocabulary size of 1 000 words the project considered speech understanding a key to achieving progress in speech recognition which was later disproved 22 bbn ibm carnegie mellon cmu and stanford research institute participated 23 24 1972 the ieee acoustics speech and signal processing group held a conference in newton massachusetts 1976 the first icassp was held in philadelphia which became a major venue for publishing on speech recognition 25 during the late 1960s leonard baum developed the mathematics of markov chains at the institute for defense analysis a decade later at cmu raj reddy s students james baker and janet m baker began using the hidden markov model hmm for speech recognition 26 james baker had learned about hmms while at the institute for defense analysis 27 hmms enabled researchers to combine sources of knowledge such as acoustics language and syntax in a unified probabilistic model by the mid 1980s fred jelinek s team at ibm created a voice activated typewriter called tangora which could handle a 20 000 word vocabulary 28 jelinek s statistical approach placed less emphasis on emulating human brain processes in favor of statistical modelling jelinek s group independently discovered the application of hmms to speech 27 this was controversial among linguists since hmms are too simplistic to account for many features of human languages 29 however the hmm proved to be a highly useful way for modelling speech and replaced dynamic time warping as the dominant speech recognition algorithm in the 1980s 30 31 1982 dragon systems founded by james and janet m baker 32 was one of ibm s few competitors practical speech recognition edit the 1980s also saw the introduction of the n gram language model 1987 the back off model enabled language models to use multiple length n grams and cselt 33 used hmm to recognize languages in software and hardware e g ripac at the end of the darpa program in 1976 the best computer available to researchers was the pdp 10 with 4 mb of ram 34 it could take up to 100 minutes to decode 30 seconds of speech 35 practical products included 1984 the apricot portable was released with up to 4096 words support of which only 64 could be held in ram at a time 36 1987 a recognizer from kurzweil applied intelligence 1990 dragon dictate a consumer product released in 1990 37 38 at t deployed the voice recognition call processing service in 1992 to route telephone calls without a human operator 39 the technology was developed by lawrence rabiner and others at bell labs by the early 1990s the vocabulary of the typical commercial speech recognition system had exceeded the average human vocabulary 34 reddy s former student xuedong huang developed the sphinx ii system at cmu sphinx ii was the first to do speaker independent large vocabulary continuous speech recognition and it won darpa s 1992 evaluation handling continuous speech with a large vocabulary was a major milestone huang later founded the speech recognition group at microsoft in 1993 reddy s student kai fu lee joined apple where in 1992 he helped develop the casper speech interface prototype lernout hauspie a belgium based speech recognition company acquired other companies including kurzweil applied intelligence in 1997 and dragon systems in 2000 l h was used in windows xp l h was an industry leader until an accounting scandal destroyed it in 2001 l h speech technology was bought by scansoft which became nuance in 2005 apple licensed nuance software for its digital assistant siri 40 2000s edit in the 2000s darpa sponsored two speech recognition programs effective affordable reusable speech to text ears in 2002 followed by global autonomous language exploitation gale in 2005 four teams participated in ears ibm a team led by bbn with limsi and the university of pittsburgh cambridge university and a team composed of icsi sri and the university of washington ears funded the collection of the switchboard telephone speech corpus which contained 260 hours of recorded conversations from over 500 speakers 41 the gale program focused on arabic and mandarin broadcast news google s first effort at speech recognition came in 2007 after recruiting nuance researchers 42 its first product goog 411 was a telephone based directory service since at least 2006 the u s national security agency has employed keyword spotting allowing analysts to index large volumes of recorded conversations and identify speech containing interesting keywords 43 other government research programs focused on intelligence applications such as darpa s ears program and iarpa s babel program in the early 2000s speech recognition was dominated by hidden markov models combined with feed forward artificial neural networks ann 44 later speech recognition was taken over by long short term memory lstm a recurrent neural network rnn published by sepp hochreiter jürgen schmidhuber in 1997 45 lstm rnns avoid the vanishing gradient problem and can learn very deep learning tasks 46 that require memories of events that happened thousands of discrete time steps earlier which is important for speech around 2007 lstms trained with connectionist temporal classification ctc 47 began to outperform 48 in 2015 google reported a 49 percent error rate reduction in its speech recognition via ctc trained lstm 49 transformers a type of neural network based solely on attention were adopted in computer vision 50 51 and language modelling 52 53 and then to speech recognition 54 55 56 deep feed forward non recurrent networks for acoustic modelling were introduced in 2009 by geoffrey hinton and his students at the university of toronto and by li deng 57 and colleagues at microsoft research 58 59 60 61 in contrast to the prioer incremental improvements deep learning decreased error rates by 30 61 both shallow and deep forms e g recurrent nets of anns had been explored since the 1980s 62 63 64 however these methods never defeated non uniform internal handcrafting gaussian mixture model hidden markov model gmm hmm technology 65 difficulties analyzed in the 1990s included gradient diminishing 66 and weak temporal correlation structure 67 68 all these difficulties combined with insufficient training data and computing power most speech recognition pursued generative modelling approaches until deep learning won the day hinton et al and deng et al 59 60 69 70 2010s edit by early the 2010s speech recognition 71 72 73 was differentiated from speaker recognition and speaker independence was considered a major breakthrough until then systems required a training period for each voice 16 in 2017 microsoft researchers reached the human parity milestone of transcribing conversational speech on the widely benchmarked switchboard task multiple deep learning models were used to improve accuracy the error rate was reported to be as low as 4 professional human transcribers working together on the same benchmark 74 models methods and algorithms edit both acoustic modeling and language modeling are important parts of statistically based speech recognition algorithms hidden markov models hmms are widely used in many systems language modelling is also used in many other natural language processing applications such as document classification or statistical machine translation hidden markov models edit main article hidden markov model speech recognition systems are based on hmms these are statistical models that output a sequence of symbols or quantities hmms are used in speech recognition because a speech signal can be viewed as a piecewise stationary signal or a short time stationary signal in a short time scale e g 10 milliseconds speech can be approximated as a stationary process speech can be thought of as a markov model for many stochastic purposes hmms are popular because they can be trained automatically and are simple and computationally feasible an hmm outputs a sequence of n dimensional real valued vectors where n is an integer such as 10 outputting one every 10 milliseconds the vectors consist of cepstral coefficients obtained by a fourier transform of a short window of speech and decorrelating the spectrum using a cosine transform then taking the first most significant coefficients the hmm tends to have in each state a statistical distribution that is a mixture of diagonal covariance gaussians which give a likelihood for each observed vector each word or for more general speech recognition systems each phoneme has a different output distribution an hmm for a sequence of words or phonemes is made by concatenating the individual trained hmms for the separate words and phonemes speech recognition systems use combinations of standard techniques to improve results a typical large vocabulary system applies context dependency for the phonemes so that phonemes with different left and right context have different realizations as hmm states it uses cepstral normalization to handle speaker and recording conditions it might use vocal tract length normalization vtln for male female normalization and maximum likelihood linear regression mllr for more general adaptation the features use delta and delta delta coefficients to capture speech dynamics and in addition might use heteroscedastic linear discriminant analysis hlda or might use splicing and lda based projection followed by hlda or a global semi tied covariance transform also known as maximum likelihood linear transform mllt many systems use discriminative training techniques that dispense with a purely statistical approach to hmm parameter estimation and instead optimize some classification related measure of the training data examples are maximum mutual information mmi minimum classification error mce and minimum phone error mpe dynamic time warping dtw based speech recognition edit main article dynamic time warping dynamic time warping was historically used for speech recognition but was later displaced by hmm dynamic time warping measures similarity between two sequences that may vary in time or speed for instance similarities in walking patterns could be detected even if in one video a person was walking slowly and in another was walking more quickly or even if accelerations and decelerations came during one observation dtw has been applied to video audio and graphics any data that can be turned into a linear representation can be analyzed with dtw this could handle speech at different speaking speeds in general it allows an optimal match between two sequences e g time series with certain restrictions the sequences are warped non linearly to match each other this sequence alignment method is often used in the context of hmms neural networks edit main article artificial neural network 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