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<title>Speech processing</title>

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<span id="openzim-page-title" class="mw-page-title-main"><span class="mw-page-title-main">Speech processing</span></span>

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</style><div role="note" class="hatnote navigation-not-searchable">This article is about electronic speech processing. For speech processing in the human brain, see <a href="Language_processing_in_the_brain" title="Language processing in the brain">Language processing in the brain</a>.</div>

<p><b>Speech processing</b> is the study of <a href="Speech_communication" class="mw-redirect" title="Speech communication">speech</a> <a href="Signal_(information_theory)" class="mw-redirect" title="Signal (information theory)">signals</a> and the processing methods of signals. The signals are usually processed in a <a href="Digital_data" title="Digital data">digital</a> representation, so speech processing can be regarded as a special case of <a href="Digital_signal_processing" title="Digital signal processing">digital signal processing</a>, applied to <a href="Audio_signal" title="Audio signal">speech signals</a>. Aspects of speech processing includes the acquisition, manipulation, storage, transfer and output of speech signals. Different speech processing tasks include <a href="Speech_recognition" title="Speech recognition">speech recognition</a>, <a href="Speech_synthesis" title="Speech synthesis">speech synthesis</a>, <a href="Speaker_diarization" class="mw-redirect" title="Speaker diarization">speaker diarization</a>, <a href="Speech_enhancement" title="Speech enhancement">speech enhancement</a>, <a href="Speaker_recognition" title="Speaker recognition">speaker recognition</a>, etc.<sup id="cite_ref-1" class="reference"><a href="#cite_note-1"><span class="cite-bracket">[</span>1<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading2"><h2 id="History">History</h2></div>

<p>Early attempts at speech processing and recognition were primarily focused on understanding a handful of simple <a href="Phonetics" title="Phonetics">phonetic</a> elements such as vowels. In 1952, three researchers at Bell Labs, Stephen. Balashek, R. Biddulph, and K. H. Davis, developed a system that could recognize digits spoken by a single speaker.<sup id="cite_ref-2" class="reference"><a href="#cite_note-2"><span class="cite-bracket">[</span>2<span class="cite-bracket">]</span></a></sup> Pioneering works in field of speech recognition using analysis of its spectrum were reported in the 1940s.<sup id="cite_ref-3" class="reference"><a href="#cite_note-3"><span class="cite-bracket">[</span>3<span class="cite-bracket">]</span></a></sup>

</p><p><a href="Linear_predictive_coding" title="Linear predictive coding">Linear predictive coding</a> (LPC), a speech processing algorithm, was first proposed by <a href="Fumitada_Itakura" title="Fumitada Itakura">Fumitada Itakura</a> of <a href="Nagoya_University" title="Nagoya University">Nagoya University</a> and Shuzo Saito of <a href="Nippon_Telegraph_and_Telephone" title="Nippon Telegraph and Telephone">Nippon Telegraph and Telephone</a> (NTT) in 1966.<sup id="cite_ref-Gray_4-0" class="reference"><a href="#cite_note-Gray-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> Further developments in LPC technology were made by <a href="Bishnu_S._Atal" title="Bishnu S. Atal">Bishnu S. Atal</a> and <a href="Manfred_R._Schroeder" title="Manfred R. Schroeder">Manfred R. Schroeder</a> at <a href="Bell_Labs" title="Bell Labs">Bell Labs</a> during the 1970s.<sup id="cite_ref-Gray_4-1" class="reference"><a href="#cite_note-Gray-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> LPC was the basis for <a href="Voice-over-IP" class="mw-redirect" title="Voice-over-IP">voice-over-IP</a> (VoIP) technology,<sup id="cite_ref-Gray_4-2" class="reference"><a href="#cite_note-Gray-4"><span class="cite-bracket">[</span>4<span class="cite-bracket">]</span></a></sup> as well as <a href="Speech_synthesizer" class="mw-redirect" title="Speech synthesizer">speech synthesizer</a> chips, such as the <a href="Texas_Instruments_LPC_Speech_Chips" title="Texas Instruments LPC Speech Chips">Texas Instruments LPC Speech Chips</a> used in the <a href="Speak_%26_Spell_(toy)" title="Speak & Spell (toy)">Speak & Spell</a> toys from 1978.<sup id="cite_ref-vintagecomputing_article_5-0" class="reference"><a href="#cite_note-vintagecomputing_article-5"><span class="cite-bracket">[</span>5<span class="cite-bracket">]</span></a></sup>

</p><p>One of the first commercially available speech recognition products was Dragon Dictate, released in 1990. In 1992, technology developed by <a href="Lawrence_Rabiner" title="Lawrence Rabiner">Lawrence Rabiner</a> and others at Bell Labs was used by <a href="AT%26T" title="AT&T">AT&T</a> in their Voice Recognition Call Processing service to route calls without a human operator. By this point, the vocabulary of these systems was larger than the average human vocabulary.<sup id="cite_ref-6" class="reference"><a href="#cite_note-6"><span class="cite-bracket">[</span>6<span class="cite-bracket">]</span></a></sup>

</p><p>By the early 2000s, the dominant speech processing strategy started to shift away from <a href="Hidden_Markov_model" title="Hidden Markov model">Hidden Markov Models</a> towards more modern <a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">neural networks</a> and <a href="Deep_learning" title="Deep learning">deep learning</a>.<sup id="cite_ref-7" class="reference"><a href="#cite_note-7"><span class="cite-bracket">[</span>7<span class="cite-bracket">]</span></a></sup>

</p><p>In 2012, <a href="Geoffrey_Hinton" title="Geoffrey Hinton">Geoffrey Hinton</a> and his team at the <a href="University_of_Toronto" title="University of Toronto">University of Toronto</a> demonstrated that deep neural networks could significantly outperform traditional HMM-based systems on large vocabulary continuous speech recognition tasks. This breakthrough led to widespread adoption of deep learning techniques in the industry.<sup id="cite_ref-:0_8-0" class="reference"><a href="#cite_note-:0-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-9" class="reference"><a href="#cite_note-9"><span class="cite-bracket">[</span>9<span class="cite-bracket">]</span></a></sup>

</p><p>By the mid-2010s, companies like <a href="Google" title="Google">Google</a>, <a href="Microsoft" title="Microsoft">Microsoft</a>, <a href="Amazon_(company)" title="Amazon (company)">Amazon</a>, and <a href="Apple_Inc." title="Apple Inc.">Apple</a> had integrated advanced speech recognition systems into their virtual assistants such as <a href="Google_Assistant" title="Google Assistant">Google Assistant</a>, <a href="Cortana_(virtual_assistant)" title="Cortana (virtual assistant)">Cortana</a>, <a href="Amazon_Alexa" title="Amazon Alexa">Alexa</a>, and <a href="Siri" title="Siri">Siri</a>.<sup id="cite_ref-10" class="reference"><a href="#cite_note-10"><span class="cite-bracket">[</span>10<span class="cite-bracket">]</span></a></sup> These systems utilized deep learning models to provide more natural and accurate voice interactions.

</p><p>The development of Transformer-based models, like Google's BERT (Bidirectional Encoder Representations from Transformers) and OpenAI's GPT (Generative Pre-trained Transformer), further pushed the boundaries of natural language processing and speech recognition. These models enabled more context-aware and semantically rich understanding of speech.<sup id="cite_ref-11" class="reference"><a href="#cite_note-11"><span class="cite-bracket">[</span>11<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-:0_8-1" class="reference"><a href="#cite_note-:0-8"><span class="cite-bracket">[</span>8<span class="cite-bracket">]</span></a></sup> In recent years, end-to-end speech recognition models have gained popularity. These models simplify the speech recognition pipeline by directly converting audio input into text output, bypassing intermediate steps like feature extraction and acoustic modeling. This approach has streamlined the development process and improved performance.<sup id="cite_ref-12" class="reference"><a href="#cite_note-12"><span class="cite-bracket">[</span>12<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading2"><h2 id="Techniques">Techniques</h2></div>

<div class="mw-heading mw-heading3"><h3 id="Dynamic_time_warping">Dynamic time warping</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Dynamic_time_warping" title="Dynamic time warping">Dynamic time warping</a></div><p>Dynamic time warping (DTW) is an <a href="Algorithm" title="Algorithm">algorithm</a> for measuring similarity between two <a href="Time_series" title="Time series">temporal sequences</a>, which may vary in speed. In general, DTW is a method that calculates an <a href="Optimal_matching" title="Optimal matching">optimal match</a> between two given sequences (e.g. time series) with certain restriction and rules. The optimal match is denoted by the match that satisfies all the restrictions and the rules and that has the minimal cost, where the cost is computed as the sum of absolute differences, for each matched pair of indices, between their values.

</p><div class="mw-heading mw-heading3"><h3 id="Hidden_Markov_models">Hidden Markov models</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Hidden_Markov_model" title="Hidden Markov model">Hidden Markov model</a></div><p>A hidden Markov model can be represented as the simplest <a href="Dynamic_Bayesian_network" title="Dynamic Bayesian network">dynamic Bayesian network</a>. The goal of the algorithm is to estimate a hidden variable x(t) given a list of observations y(t). By applying the <a href="Markov_property" title="Markov property">Markov property</a>, the <a href="Conditional_probability_distribution" title="Conditional probability distribution">conditional probability distribution</a> of the hidden variable <i>x</i>(<i>t</i>) at time <i>t</i>, given the values of the hidden variable <i>x</i> at all times, depends <i>only</i> on the value of the hidden variable <i>x</i>(<i>t</i> − 1). Similarly, the value of the observed variable <i>y</i>(<i>t</i>) only depends on the value of the hidden variable <i>x</i>(<i>t</i>) (both at time <i>t</i>).

</p><div class="mw-heading mw-heading3"><h3 id="Artificial_neural_networks">Artificial neural networks</h3></div>

<div role="note" class="hatnote navigation-not-searchable">Main article: <a href="Artificial_neural_network" class="mw-redirect" title="Artificial neural network">Artificial neural network</a></div><p>An artificial neural network (ANN) is based on a collection of connected units or nodes called <a href="Artificial_neuron" title="Artificial neuron">artificial neurons</a>, which loosely model the <a href="Neuron" title="Neuron">neurons</a> in a biological <a href="Brain" title="Brain">brain</a>. Each connection, like the <a href="Synapse" title="Synapse">synapses</a> in a biological <a href="Brain" title="Brain">brain</a>, can transmit a signal from one artificial neuron to another. An artificial neuron that receives a signal can process it and then signal additional artificial neurons connected to it. In common ANN implementations, the signal at a connection between artificial neurons is a <a href="Real_number" title="Real number">real number</a>, and the output of each artificial neuron is computed by some non-linear function of the sum of its inputs.

</p><div class="mw-heading mw-heading3"><h3 id="Phase-aware_processing">Phase-aware processing</h3></div>

<p>Phase is often assumed to be random, but contains useful information. Wrapping of phase:<sup id="cite_ref-limits_13-0" class="reference"><a href="#cite_note-limits-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup> can be introduced due to periodical jumps on <span class="mwe-math-element mwe-math-element-inline"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle 2\pi }">

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<annotation encoding="application/x-tex">{\displaystyle 2\pi }</annotation>

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</math></span><img src="./73efd1f6493490b058097060a572606d2c550a06.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.338ex; width:2.494ex; height:2.176ex;" alt="{\displaystyle 2\pi }" loading="lazy"></span>. Phase unwrapping (see,<sup id="cite_ref-14" class="reference"><a href="#cite_note-14"><span class="cite-bracket">[</span>14<span class="cite-bracket">]</span></a></sup> Chapter 2.3; <a href="Instantaneous_phase_and_frequency" title="Instantaneous phase and frequency">Instantaneous phase and frequency</a>), it can be expressed as:<sup id="cite_ref-limits_13-1" class="reference"><a href="#cite_note-limits-13"><span class="cite-bracket">[</span>13<span class="cite-bracket">]</span></a></sup><sup id="cite_ref-vonMises_15-0" class="reference"><a href="#cite_note-vonMises-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup>

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<annotation encoding="application/x-tex">{\displaystyle \phi (h,l)=\phi _{lin}(h,l)+\Psi (h,l)}</annotation>

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</math></span><img src="./27b9b179b1f238a7ccfc6078665851084b3b5201.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:27.42ex; height:2.843ex;" alt="{\displaystyle \phi (h,l)=\phi _{lin}(h,l)+\Psi (h,l)}" loading="lazy"></span>, where <span class="mwe-math-element mwe-math-element-inline"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \phi _{lin}(h,l)=\omega _{0}(l'){}_{\Delta }t}">

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<msup>

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<annotation encoding="application/x-tex">{\displaystyle \phi _{lin}(h,l)=\omega _{0}(l'){}_{\Delta }t}</annotation>

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</math></span><img src="./1d2c51a02117724ca424de1acd659f1702f9c3d4.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:19.764ex; height:3.009ex;" alt="{\displaystyle \phi _{lin}(h,l)=\omega _{0}(l'){}_{\Delta }t}" loading="lazy"></span> is linear phase (<span class="mwe-math-element mwe-math-element-inline"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle {}_{\Delta }t}">

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<annotation encoding="application/x-tex">{\displaystyle {}_{\Delta }t}</annotation>

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</math></span><img src="./88ff57f69723ccb8153df6cdb30b478fed75e41d.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.671ex; width:2.441ex; height:2.343ex;" alt="{\displaystyle {}_{\Delta }t}" loading="lazy"></span> is temporal shift at each frame of analysis), <span class="mwe-math-element mwe-math-element-inline"><span class="mwe-math-mathml-inline mwe-math-mathml-a11y" style="display: none;"><math xmlns="http://www.w3.org/1998/Math/MathML" alttext="{\displaystyle \Psi (h,l)}">

</mstyle>

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<annotation encoding="application/x-tex">{\displaystyle \Psi (h,l)}</annotation>

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</math></span><img src="./38ddf67a07c5d6a09c7131a612edeeb38ac022e3.svg" class="mwe-math-fallback-image-inline mw-invert skin-invert" aria-hidden="true" style="vertical-align: -0.838ex; width:6.684ex; height:2.843ex;" alt="{\displaystyle \Psi (h,l)}" loading="lazy"></span> is phase contribution of the vocal tract and phase source.<sup id="cite_ref-vonMises_15-1" class="reference"><a href="#cite_note-vonMises-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup>

Obtained phase estimations can be used for noise reduction: temporal smoothing of instantaneous phase <sup id="cite_ref-16" class="reference"><a href="#cite_note-16"><span class="cite-bracket">[</span>16<span class="cite-bracket">]</span></a></sup> and its derivatives by time (<a href="Instantaneous_phase_and_frequency" title="Instantaneous phase and frequency">instantaneous frequency</a>) and frequency (<a href="Group_delay_and_phase_delay" title="Group delay and phase delay">group delay</a>),<sup id="cite_ref-Advances_17-0" class="reference"><a href="#cite_note-Advances-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup> smoothing of phase across frequency.<sup id="cite_ref-Advances_17-1" class="reference"><a href="#cite_note-Advances-17"><span class="cite-bracket">[</span>17<span class="cite-bracket">]</span></a></sup> Joined amplitude and phase estimators can recover speech more accurately basing on assumption of von Mises distribution of phase.<sup id="cite_ref-vonMises_15-2" class="reference"><a href="#cite_note-vonMises-15"><span class="cite-bracket">[</span>15<span class="cite-bracket">]</span></a></sup>

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<div class="mw-heading mw-heading2"><h2 id="Applications">Applications</h2></div>

<ul><li><a href="Interactive_voice_response" title="Interactive voice response">Interactive voice response</a></li>

<li><a href="Virtual_assistant" title="Virtual assistant">Virtual Assistants</a></li>

<li><a href="Speaker_recognition" title="Speaker recognition">Voice Identification</a></li>

<li><a href="Emotion_recognition" title="Emotion recognition">Emotion Recognition</a></li>

<li>Call Center Automation</li>

<li><a href="Robotics" title="Robotics">Robotics</a></li></ul>

<ul><li><a href="Computational_audiology" title="Computational audiology">Computational audiology</a></li>

<li><a href="Neurocomputational_speech_processing" title="Neurocomputational speech processing">Neurocomputational speech processing</a></li>

<li><a href="Speech_coding" title="Speech coding">Speech coding</a></li>

<li><a href="Speech_technology" title="Speech technology">Speech technology</a></li>

<li><a href="Natural_language_processing" title="Natural language processing">Natural Language Processing</a></li></ul>

<div class="mw-heading mw-heading2"><h2 id="References">References</h2></div>

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</style><cite id="CITEREFSahidullahPatinoCornellYin2019" class="citation arxiv cs1">Sahidullah, Md; Patino, Jose; Cornell, Samuele; Yin, Ruiking; Sivasankaran, Sunit; Bredin, Herve; Korshunov, Pavel; Brutti, Alessio; Serizel, Romain; Vincent, Emmanuel; Evans, Nicholas; Marcel, Sebastien; Squartini, Stefano; Barras, Claude (2019-11-06). "The Speed Submission to DIHARD II: Contributions & Lessons Learned". <a href="ArXiv_(identifier)" class="mw-redirect" title="ArXiv (identifier)">arXiv</a>:<span class="id-lock-free" title="Freely accessible"><a rel="nofollow" class="external text" href="https://arxiv.org/abs/1911.02388">1911.02388</a></span> [<a rel="nofollow" class="external text" href="https://arxiv.org/archive/eess.AS">eess.AS</a>].</cite></span>

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<li id="cite_note-2"><span class="mw-cite-backlink"><b><a href="#cite_ref-2">^</a></b></span> <span class="reference-text"><cite id="CITEREFJuangRabiner2006" class="citation cs2">Juang, B.-H.; Rabiner, L.R. (2006), "Speech Recognition, Automatic: History", <i>Encyclopedia of Language & Linguistics</i>, Elsevier, pp. <span class="nowrap">806–</span>819, <a href="Doi_(identifier)" class="mw-redirect" title="Doi (identifier)">doi</a>:<a rel="nofollow" class="external text" href="https://doi.org/10.1016%2Fb0-08-044854-2%2F00906-8">10.1016/b0-08-044854-2/00906-8</a>, <a href="ISBN_(identifier)" class="mw-redirect" title="ISBN (identifier)">ISBN</a> <bdi>9780080448541</bdi></cite></span>

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<li id="cite_note-3"><span class="mw-cite-backlink"><b><a href="#cite_ref-3">^</a></b></span> <span class="reference-text"><cite id="CITEREFMyasnikovMyasnikova1970" class="citation book cs1 cs1-prop-foreign-lang-source">Myasnikov, L. L.; Myasnikova, Ye. N. (1970). <i>Automatic recognition of sound pattern</i> (in Russian). Leningrad: Energiya.</cite></span>

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</style><div id="Computer_audition21" style="font-size:114%;margin:0 4em"><a href="Computer_audition" title="Computer audition">Computer audition</a></div></th></tr><tr><td colspan="2" class="navbox-list navbox-odd hlist" style="width:100%;padding:0"><div style="padding:0 0.25em">

<ul><li><a href="Acoustic_fingerprint" title="Acoustic fingerprint">Acoustic fingerprint</a></li>

<li><a href="Audio_mining" title="Audio mining">Audio mining</a></li>

<li><a href="Computational_auditory_scene_analysis" title="Computational auditory scene analysis">Computational auditory scene analysis</a></li>

<li><a href="Music_information_retrieval" title="Music information retrieval">Music information retrieval</a></li>

<li><a href="Semantic_audio" title="Semantic audio">Semantic audio</a></li>

<li>

<ul><li><a href="Speech_analytics" title="Speech analytics">Speech analytics</a></li>

<li><a href="Speaker_recognition" title="Speaker recognition">Speaker recognition</a></li>

<li><a href="Speech_recognition" title="Speech recognition">Speech recognition</a></li></ul></li>

<li><a href="Sound_recognition" title="Sound recognition">Sound recognition</a></li>

<li><a href="3D_sound_localization" title="3D sound localization">3D sound localization</a></li>

<li><a href="3D_sound_reconstruction" title="3D sound reconstruction">3D sound reconstruction</a></li></ul>

</div></td></tr></tbody></table></div>

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