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d 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 neural networks became interesting in the late 1980s before beginning to dominate in the 2010s neural networks have been used in many aspects of speech recognition such as phoneme classification 75 phoneme classification through multi objective evolutionary algorithms 76 isolated word recognition 77 audiovisual speech recognition audiovisual speaker recognition and speaker adaptation neural networks make fewer explicit assumptions about feature statistical properties than hmms when used to estimate the probabilities of a speech segment neural networks allow natural and efficient discriminative training however in spite of their effectiveness in classifying short time units such as individual phonemes and isolated words 78 early neural networks were rarely successful for continuous recognition because of their limited ability to model temporal dependencies one approach was to use neural networks for feature transformation or dimensionality reduction 79 however more recently lstm and related recurrent neural networks rnns 45 49 80 81 time delay neural networks tdnn s 82 and transformers 54 55 56 demonstrated improved performance deep feedforward and recurrent neural networks edit main article deep learning researchers are exploring deep neural networks dnns and denoising autoencoders 83 a dnn is a type of artificial neural network that includes multiple hidden layers between the input and output 59 like simpler neural networks dnns can model complex non linear relationships however their deeper architecture allows them to build more sophisticated representations that combine features from earlier layers this gives them a powerful ability to learn and recognize complex patterns in speech data 84 a major breakthrough in using dnns for large vocabulary speech recognition came in 2010 in a collaboration between industry and academia researchers used dnns with large output layers based on context dependent hmm states that were created using decision trees 85 86 87 this approach significantly improved performance 88 89 90 a core idea behind deep learning is to eliminate the need for manually designed features and instead learn directly from input data this was first demonstrated using deep autoencoders trained on raw spectrograms or linear filter bank features 91 these models outperformed traditional mel cepstral features which rely on fixed transformations more recently researchers showed that waveforms can achieve excellent results in large scale speech recognition 92 end to end learning edit since 2014 much research has considered end to end asr traditional phonetic based i e all hmm based model approaches required separate components and training for pronunciation acoustic and language end to end models learn from all the components at once this simplifies the training and deployment processes for example an n gram language model is required for all hmm based systems and a typical 2025 era n gram language model often takes gigabytes in memory making them impractical to deploy on mobile devices 93 consequently asr systems from google and apple as of 2017 update deploy on servers and required a network connection to operate 94 the first attempt at end to end asr was the connectionist temporal classification ctc based system introduced by alex graves of google deepmind and navdeep jaitly of the university of toronto in 2014 95 the model consisted of rnns and a ctc layer jointly the rnn ctc model learns the pronunciation and acoustic model together however it is incapable of learning the language model due to conditional independence assumptions similar to an hmm consequently ctc models can directly learn to map speech acoustics to english characters but the models make many common spelling mistakes and must rely on a separate language model to finalize transcripts later baidu expanded on the work with extremely large datasets and demonstrated some commercial success in mandarin and english 96 in 2016 the university of oxford presented lipnet 97 the first end to end sentence level lipreading model using spatiotemporal convolutions coupled with an rnn ctc architecture surpassing human level performance in a restricted dataset 98 a large scale convolutional rnn ctc architecture was presented in 2018 by google deepmind achieving 6 times better performance than human experts 99 in 2019 nvidia launched two cnn ctc asr models jasper and quarznet with an overall performance word error rate wer of 3 100 101 similar to other deep learning applications transfer learning and domain adaptation are important strategies for reusing and extending the capabilities of deep learning models particularly due to the small size of available corpora in many languages and or specific domains 102 103 104 in 2018 researchers at mit media lab announced preliminary work on alterego a device that uses electrodes to read the neuromuscular signals users make as they subvocalize 105 they trained a convolutional neural network to translate the electrode signals into words 106 attention based models edit attention based asr models were introduced by chan et al of carnegie mellon university and google brain and bahdanau et al of the university of montreal in 2016 107 108 the model named listen attend and spell las literally listens to the acoustic signal pays attention to all parts of the signal and spells out the transcript one character at a time unlike ctc based models attention based models require conditional independence assumptions and can learn all the components of a speech recognizer directly this means that during deployment no a priori language model is required making it less demanding for applications with limited memory attention based models immediately outperformed ctc models with or without an external language model and continued improving 109 latent sequence decomposition lsd was proposed by carnegie mellon university mit and google brain to directly emit sub word units that are more natural than english characters 110 the university of oxford and google deepmind extended las to watch listen attend and spell wlas to handle lip reading and surpassed human level performance 111 applications edit in car systems edit voice commands may be used to initiate phone calls select radio stations or play music voice recognition capabilities vary across car make and model some models offer natural language speech recognition allowing the driver to use full sentences and common phrases in a conversational style with such systems fixed commands are not required 112 education edit main article pronunciation assessment automatic pronunciation assessment is the use of speech recognition to verify the correctness of speech 113 as distinguished from assessment by a person 114 also called speech verification pronunciation evaluation and pronunciation scoring the main application of this technology is computer aided pronunciation teaching capt when combined with computer aided instruction for computer assisted language learning call speech remediation or accent reduction pronunciation assessment does not determine unknown speech as in dictation or automatic transcription but instead compares speech to a reference model for the words spoken 115 116 sometimes with inconsequential prosody such as intonation pitch tempo rhythm and stress 117 pronunciation assessment is also used in reading tutoring for example in products such as microsoft teams 118 and amira learning 119 pronunciation assessment can also be used to help diagnose and treat speech disorders such as apraxia 120 assessing intelligibility is essential for avoiding inaccuracies from accent bias especially in high stakes assessments 121 122 123 from words 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