Speech recognition

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For the human linguistic concept, see Speech perception.

Speech recognition (SR) is the inter-disciplinary sub-field of computational linguistics which incorporates knowledge and research in the linguistics, computer science, and electrical engineering fields to develop methodologies and technologies that enables the recognition and translation of spoken language into text by computers and computerized devices such as those categorized as smart technologies and robotics. It is also known as "automatic speech recognition" (ASR), "computer speech recognition", or just "speech to text" (STT).




From the technology perspective, speech recognition has a long history with several waves of major innovations. Most recently, the field has benefited from advances in deep learning and big data. The advances are evidenced not only by the surge of academic papers published in the field, but more importantly by the world-wide industry adoption of a variety of deep learning methods in designing and deploying speech recognition systems. These speech industry players include Google, Microsoft, Hewlett Packard Enterprise,[7] IBM, Baidu (China), Apple, Amazon, Nuance, IflyTek (China), many of which have publicized the core technology in their speech recognition systems as being based on deep learning.


As early as 1932, Bell Labs researchers like Harvey Fletcher were investigating the science of speech perception.[8] In 1952 three Bell Labs researchers built a system for single-speaker digit recognition. Their system worked by locating the formants in the power spectrum of each utterance.[9] The 1950s era technology was limited to single-speaker systems with vocabularies of around ten words.

Unfortunately, funding at Bell Labs dried up for several years when, in 1969, the influential John Pierce wrote an open letter that was critical of speech recognition research.[10] Pierce's letter compared speech recognition to "schemes for turning water into gasoline, extracting gold from the sea, curing cancer, or going to the moon." Pierce defunded speech recognition research at Bell Labs.

Raj Reddy was the first person to take on continuous speech recognition as a graduate student at Stanford University in the late 1960s. Previous systems required the users to make a pause after each word. Reddy's system was designed to issue spoken commands for the game of chess. Also around this time Soviet researchers invented the dynamic time warping algorithm and used it to create a recognizer capable of operating on a 200-word vocabulary.[11] Achieving speaker independence was a major unsolved goal of researchers during this time period.

In 1971, DARPA funded five years of speech recognition research through its Speech Understanding Research program with ambitious end goals including a minimum vocabulary size of 1,000 words. BBN, IBM, Carnegie Mellon and Stanford Research Institute all participated in the program.[12][13] The government funding revived speech recognition research that had been largely abandoned in the United States after John Pierce's letter. Despite the fact that CMU's Harpy system met the goals established at the outset of the program, many of the predictions turned out to be nothing more than hype, disappointing DARPA administrators. This disappointment led to DARPA not continuing the funding.[14] Several innovations happened during this time, such as the invention of beam search for use in CMU's Harpy system.[15] The field also benefited from the discovery of several algorithms in other fields such as linear predictive coding and cepstral analysis.

During the late 1960s Leonard Baum developed the mathematics of Markov chains at the Institute for Defense Analysis. At CMU, Raj Reddy's students James Baker and Janet Baker began using the Hidden Markov Model (HMM) for speech recognition.[16] James Baker had learned about HMMs from a summer job at the Institute of Defense Analysis during his undergraduate education.[17] The use of HMMs allowed researchers to combine different sources of knowledge, such as acoustics, language, and syntax, in a unified probabilistic model.

Under Fred Jelinek's lead, IBM created a voice activated typewriter called Tangora, which could handle a 20,000 word vocabulary by the mid 1980s.[18] Jelinek's statistical approach put less emphasis on emulating the way the human brain processes and understands speech in favor of using statistical modeling techniques like HMMs. (Jelinek's group independently discovered the application of HMMs to speech.[19]) This was controversial with linguists since HMMs are too simplistic to account for many common features of human languages.[20] However, the HMM proved to be a highly useful way for modeling speech and replaced dynamic time warping to become the dominate speech recognition algorithm in the 1980s.[21] IBM had a few competitors including Dragon Systems founded by James and Janet Baker in 1982.[22] The 1980s also saw the introduction of the n-gram language model. Katz introduced the back-off model in 1987, which allowed language models to use multiple length n-grams. During the same time, also CSELT was using HMM (especially, the diphonies) to recognize language like Italian.[23][24][25] At the same time, CSELT led a series of European projects (Esprit I, II), and summarized the state-of-the-art in a book, later (2013) reprinted.[26]

Much of the progress in the field is owed to the rapidly increasing capabilities of computers. At the end of the DARPA program in 1976, the best computer available to researchers was the PDP-10 with 4 MB ram.[20] Using these computers it could take up to 100 minutes to decode just 30 seconds of speech.[27] A few decades later, researchers had access to tens of thousands of times as much computing power. As the technology advanced and computers got faster, researchers began tackling harder problems such as larger vocabularies, speaker independence, noisy environments and conversational speech. In particular, this shifting to more difficult tasks has characterized DARPA funding of speech recognition since the 1980s. For example, progress was made on speaker independence first by training on a larger variety of speakers and then later by doing explicit speaker adaptation during decoding. Further reductions in word error rate came as researchers shifted acoustic models to be discriminative instead of using maximum likelihood models.[28]

Another one of Raj Reddy's former students, Xuedong Huang, developed the Sphinx-II system at CMU. The Sphinx-II system was the first to do speaker-independent, large vocabulary, continuous speech recognition and it had the best performance in DARPA's 1992 evaluation. Huang went on to found the speech recognition group at Microsoft in 1993.

The 1990s saw the first introduction of commercially successful speech recognition technologies. By this point, the vocabulary of the typical commercial speech recognition system was larger than the average human vocabulary.[20] In 2000, Lernout & Hauspie acquired Dragon Systems and was an industry leader until an accounting scandal brought an end to the company in 2001. The L&H speech technology was bought by ScanSoft which became Nuance in 2005. Apple originally licensed software from Nuance to provide speech recognition capability to its digital assistant Siri.[29]

21st Century[edit]

In the 2000s DARPA sponsored two speech recognition programs: Effective Affordable Reusable Speech-to-Text (EARS) in 2002 and Global Autonomous Language Exploitation (GALE). Four teams participated in the EARS program: IBM, a team led by BBN with LIMSI and Univ. of Pittsburgh, Cambridge University, and a team composed of ISCI, SRI and University of Washington. The GALE program focused on Arabic and Mandarin broadcast news speech. Google's first effort at speech recognition came in 2007 after hiring some researchers from Nuance.[30] The first product was GOOG-411, a telephone based directory service. The recordings from GOOG-411 produced valuable data that helped Google improve their recognition systems. Google voice search is now supported in over 30 languages.


In the early 2000s, speech recognition was still dominated by traditional approaches such as Hidden Markov Models combined with feedforward artificial neural networks.[32] Today, however, many aspects of speech recognition have been taken over by a deep learning method called Long short term memory (LSTM), a recurrent neural network published by Sepp Hochreiter & Jürgen Schmidhuber in 1997.[33] LSTM RNNs avoid the vanishing gradient problem and can learn "Very Deep Learning" tasks[34] that require memories of events that happened thousands of discrete time steps ago, which is important for speech. Around 2007, LSTM trained by Connectionist Temporal Classification (CTC)[35] started to outperform traditional speech recognition in certain applications.[36] In 2015, Google's speech recognition reportedly experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to all smartphone users.[37]

The use of deep feedforward (non-recurrent) networks for acoustic modeling was introduced during later part of 2009 by Geoffrey Hinton and his students at University of Toronto and by Li Deng and colleagues at Microsoft Research, initially in the collaborative work between Microsoft and University of Toronto which was subsequently expanded to include IBM and Google (hence "The shared views of four research groups" subtitle in their 2012 review paper).[38][39][40] A Microsoft research executive called this innovation "the most dramatic change in accuracy since 1979."[41] In contrast to the steady incremental improvements of the past few decades, the application of deep learning decreased word error rate by 30%. This innovation was quickly adopted across the field. Researchers have begun to use deep learning techniques for language modeling as well.

In the long history of speech recognition, both shallow form and deep form (e.g. recurrent nets) of artificial neural networks had been explored for many years during 80's, 90's and a few years into 2000.[42][43][44] But these methods never won over the non-uniform internal-handcrafting Gaussian mixture model/Hidden Markov model (GMM-HMM) technology based on generative models of speech trained discriminatively.[45] A number of key difficulties had been methodologically analyzed in the 1990s, including gradient diminishing[46] and weak temporal correlation structure in the neural predictive models.[47][48] All these difficulties were in addition to the lack of big training data and big computing power in these early days. Most speech recognition researchers who understood such barriers hence subsequently moved away from neural nets to pursue generative modeling approaches until the recent resurgence of deep learning starting around 2009-2010 that had overcome all these difficulties. Hinton et al. and Deng et al. reviewed part of this recent history about how their collaboration with each other and then with colleagues across four groups (University of Toronto, Microsoft, Google, and IBM) ignited a renaissance of applications of deep feedforward neural networks to speech recognition.[39][40][49][50]

Models, methods, and algorithms[edit]


Hidden Markov models[edit]

Main article: Hidden Markov model







Main article: Dynamic time warping




Neural networks[edit]

Main article: Neural networks

Neural networks emerged as an attractive acoustic modeling approach in ASR in the late 1980s. Since then, neural networks have been used in many aspects of speech recognition such as phoneme classification,[53] isolated word recognition,[54] and speaker adaptation.


However, recently LSTM Recurrent Neural Networks (RNNs)[33][37][56][57] and Time Delay Neural Networks(TDNN's)[58] have been used which have been shown to be able to identify latent temporal dependencies and use this information to perform the task of speech recognition.

Deep Neural Networks and Denoising Autoencoders[59] were also being experimented with to tackle this problem in an effective manner.

Due to the inability of feedforward Neural Networks to model temporal dependencies, an alternative approach is to use neural networks as a pre-processing e.g. feature transformation, dimensionality reduction,[60] for the HMM based recognition.

Deep Feedforward and Recurrent Neural Networks[edit]

Main article: Deep Learning

A deep feedforward neural network (DNN) is an artificial neural network with multiple hidden layers of units between the input and output layers.[39] Similar to shallow neural networks, DNNs can model complex non-linear relationships. DNN architectures generate compositional models, where extra layers enable composition of features from lower layers, giving a huge learning capacity and thus the potential of modeling complex patterns of speech data.[61]

A success of DNNs in large vocabulary speech recognition occurred in 2010 by industrial researchers, in collaboration with academic researchers, where large output layers of the DNN based on context dependent HMM states constructed by decision trees were adopted.[62][63] [64] See comprehensive reviews of this development and of the state of the art as of October 2014 in the recent Springer book from Microsoft Research.[65] See also the related background of automatic speech recognition and the impact of various machine learning paradigms including notably deep learning in recent overview articles.[66][67]


Many aspects of speech recognition have been taken over by a deep learning method called Long short term memory (LSTM), a recurrent neural network published by Hochreiter & Schmidhuber in 1997.[33] LSTM is normally augmented by recurrent gates called forget gates.[70] LSTM RNNs avoid the vanishing gradient problem[46] and can learn "Very Deep Learning" tasks[34] that require memories of events that happened thousands of discrete time steps ago, which is very important for speech recognition. Many applications use stacks of LSTM RNNs[56] and train them by Connectionist Temporal Classification (CTC)[35] to find an RNN weight matrix that maximizes the probability of the label sequences in a training set, given the corresponding input sequences. CTC achieves both alignment and recognition. Around 2007, CTC-trained LSTM outperformed for the first time traditional speech recognition in certain applications.[36] In 2014, the Chinese search giant Baidu used CTC-trained RNNs to break the Switchboard Hub5'00 speech recognition benchmark, without using any traditional speech processing methods.[71] LSTM also improved large-vocabulary speech recognition,[72][73] text-to-speech synthesis,[74] also for Google Android,[34][75] and photo-real talking heads.[74] In 2015, Google's speech recognition reportedly experienced a dramatic performance jump of 49% through CTC-trained LSTM, which is now available through Google Voice to all smartphone users.[37] Speech conferences such as INTERSPEECH and ICASSP have published numerous LSTM applications.

Since the initial successful debut of DNNs for speech recognition around 2009-2011 and of LSTM around 2007, there have been huge new progresses made. This progress (as well as future directions) has been summarized into the following eight major areas:[50][61][65]

  1. Scaling up/out and speedup DNN training and decoding;
  2. Sequence discriminative training of DNNs;
  3. Feature processing by deep models with solid understanding of the underlying mechanisms;
  4. Adaptation of DNNs and of related deep models;
  5. Multi-task and transfer learning by DNNs and related deep models;
  6. Convolution neural networks and how to design them to best exploit domain knowledge of speech;
  7. Recurrent neural network and its rich LSTM variants;
  8. Other types of deep models including tensor-based models and integrated deep generative/discriminative models.

Large-scale automatic speech recognition is the first and the most convincing successful case of deep learning in the recent history, embraced by both industry and academic across the board. Between 2010 and 2014, the two major conferences on signal processing and speech recognition, IEEE-ICASSP and Interspeech, have seen near exponential growth in the numbers of accepted papers in their respective annual conference papers on the topic of deep learning for speech recognition. More importantly, all major commercial speech recognition systems (e.g., Microsoft Cortana, Xbox, Skype Translator, Google Now, Apple Siri, Baidu and iFlyTek voice search, and a range of Nuance speech products, etc.) nowadays are based on deep learning methods.[61][71][76] See also the recent media interview with the CTO of Nuance Communications.[77]


In-car systems[edit]



Health care[edit]

Medical documentation[edit]




As an alternative to this navigation by hand, cascaded use of speech recognition and information extraction has been studied[78] as a way to fill out a handover form for clinical proofing and sign-off. The results are encouraging, and the paper also opens data, together with the related performance benchmarks and some processing software, to the research and development community for studying clinical documentation and language-processing.

Therapeutic use[edit]



High-performance fighter aircraft[edit]




Speaker-independent systems are also being developed and are in testing for the F35 Lightning II (JSF) and the Alenia Aermacchi M-346 Master lead-in fighter trainer. These systems have produced word accuracy in excess of 98%.[81]




Training air traffic controllers[edit]



Telephony and other domain[edit]



Usage in education and daily life[edit]






People with disabilities[edit]




Further applications[edit]


However, speech recognition (by a machine) is a very complex problem. Vocalizations vary in terms of accent, pronunciation, articulation, roughness, nasality, pitch, volume, and speed. Speech is distorted by a background noise and echoes, electrical characteristics. Accuracy of speech recognition vary with the following[93][citation needed]:

  • Vocabulary size and confusability
  • Speaker dependence vs. independence
  • Isolated, discontinuous, or continuous speech
  • Task and language constraints
  • Read vs. spontaneous speech
  • Adverse conditions



  • Error rates increase as the vocabulary size grows:


  • Vocabulary is hard to recognize if it contains confusable words:


  • Speaker dependence vs. independence:


  • Isolated, Discontinuous or continuous speech


  • Task and language constraints

e.g. Syntactic; rejecting "Red is apple the."
Constraints are often represented by a grammar.

  • Read vs. Spontaneous Speech


  • Adverse conditions

Acoustical distortions (e.g. echoes, room acoustics)
Speech recognition is a multi-levelled pattern recognition task.

  • Acoustical signals are structured into a hierarchy of units;

e.g. Phonemes, Words, Phrases, and Sentences;

  • Each level provides additional constraints;


  • This hierarchy of constraints are exploited;


  • Digitize the speech that we want to recognize

For telephone speech the sampling rate is 8000 samples per second;





  • Neural network classifies features into phonetic-based categories;

Given basic sound blocks that a machine digitized, one has a bunch of numbers which describe a wave and waves describe words. Each frame has a unit block of sound, which are broken into basic sound waves and represented by numbers which, after Fourier Transform, can be statistically evaluated to set to which class of sounds it belongs. The nodes in the figure on a slide represent a feature of a sound in which a feature of a wave from the first layer of nodes to the second layer of nodes based on statistical analysis. This analysis depends on programmer's instructions. At this point, a second layer of nodes represents higher level features of a sound input which is again statistically evaluated to see what class they belong to. Last level of nodes should be output nodes that tell us with high probability what original sound really was.


Further information[edit]

Conferences and Journals[edit]





The most recent book on speech recognition is "Automatic Speech Recognition: A Deep Learning Approach" (Publisher: Springer) written by D. Yu and L. Deng published near the end of 2014, with highly mathematically-oriented technical detail on how deep learning methods are derived and implemented in modern speech recognition systems based on DNNs and related deep learning methods.[65] A related book, published earlier in 2014, "Deep Learning: Methods and Applications" by L. Deng and D. Yu provides a less technical but more methodology-focused overview of DNN-based speech recognition during 2009-2014, placed within the more general context of deep learning applications including not only speech recognition but also image recognition, natural language processing, information retrieval, multimodal processing, and multitask learning.[61]



For more software resources, see List of speech recognition software.

See also[edit]


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Further reading[edit]


External links[edit]