Significance of the Modified Group Delay Feature in Speech Recognition
Spectral representation of speech is complete when both the Fourier transform magnitude and phase spectra are specified. In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech per...
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Veröffentlicht in: | IEEE transactions on audio, speech, and language processing speech, and language processing, 2007-01, Vol.15 (1), p.190-202 |
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creator | Hegde, R.M. Murthy, H.A. Gadde, V.R.R. |
description | Spectral representation of speech is complete when both the Fourier transform magnitude and phase spectra are specified. In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech perception has been realized, few attempts have been made to extract features from it. This is primarily because the resonances of the speech signal which manifest as transitions in the phase spectrum are completely masked by the wrapping of the phase spectrum. Hence, an alternative to processing the Fourier transform phase, for extracting speech features, is to process the group delay function which can be directly computed from the speech signal. The group delay function has been used in earlier efforts, to extract pitch and formant information from the speech signal. In all these efforts, no attempt was made to extract features from the speech signal and use them for speech recognition applications. This is primarily because the group delay function fails to capture the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects. In this paper, the group delay function is modified to overcome these effects. Cepstral features are extracted from the modified group delay function and are called the modified group delay feature (MODGDF). The MODGDF is used for three speech recognition tasks namely, speaker, language, and continuous-speech recognition. Based on the results of feature and performance evaluation, the significance of the MODGDF as a new feature for speech recognition is discussed |
doi_str_mv | 10.1109/TASL.2006.876858 |
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In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech perception has been realized, few attempts have been made to extract features from it. This is primarily because the resonances of the speech signal which manifest as transitions in the phase spectrum are completely masked by the wrapping of the phase spectrum. Hence, an alternative to processing the Fourier transform phase, for extracting speech features, is to process the group delay function which can be directly computed from the speech signal. The group delay function has been used in earlier efforts, to extract pitch and formant information from the speech signal. In all these efforts, no attempt was made to extract features from the speech signal and use them for speech recognition applications. This is primarily because the group delay function fails to capture the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects. In this paper, the group delay function is modified to overcome these effects. Cepstral features are extracted from the modified group delay function and are called the modified group delay feature (MODGDF). The MODGDF is used for three speech recognition tasks namely, speaker, language, and continuous-speech recognition. Based on the results of feature and performance evaluation, the significance of the MODGDF as a new feature for speech recognition is discussed</description><identifier>ISSN: 1558-7916</identifier><identifier>ISSN: 2329-9290</identifier><identifier>EISSN: 1558-7924</identifier><identifier>EISSN: 2329-9304</identifier><identifier>DOI: 10.1109/TASL.2006.876858</identifier><identifier>CODEN: ITASD8</identifier><language>eng</language><publisher>Piscataway, NJ: IEEE</publisher><subject>Applied sciences ; Class separability ; Data mining ; Delay effects ; Exact sciences and technology ; Feature extraction ; feature selection ; Fourier transforms ; Gaussian mixture models (GMMs) ; Group delay ; group delay function ; hidden Markov models (HMMs) ; Information, signal and communications theory ; Mathematical analysis ; Mathematical models ; Miscellaneous ; Pattern recognition ; phase spectrum ; Phase transformations ; Resonance ; robustness ; Signal and communications theory ; Signal processing ; Signal representation. Spectral analysis ; Signal, noise ; Spectra ; Speech ; Speech coding ; Speech processing ; Speech recognition ; Telecommunications and information theory ; Voice recognition ; Wrapping</subject><ispartof>IEEE transactions on audio, speech, and language processing, 2007-01, Vol.15 (1), p.190-202</ispartof><rights>2007 INIST-CNRS</rights><rights>Copyright The Institute of Electrical and Electronics Engineers, Inc. (IEEE) 2007</rights><lds50>peer_reviewed</lds50><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c418t-a0058996029595443795d7bf5c13e1c1dd983873de2017528ee21928d583cb2c3</citedby><cites>FETCH-LOGICAL-c418t-a0058996029595443795d7bf5c13e1c1dd983873de2017528ee21928d583cb2c3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://ieeexplore.ieee.org/document/4032772$$EHTML$$P50$$Gieee$$H</linktohtml><link.rule.ids>315,781,785,797,4025,27927,27928,27929,54762</link.rule.ids><linktorsrc>$$Uhttps://ieeexplore.ieee.org/document/4032772$$EView_record_in_IEEE$$FView_record_in_$$GIEEE</linktorsrc><backlink>$$Uhttp://pascal-francis.inist.fr/vibad/index.php?action=getRecordDetail&idt=18377174$$DView record in Pascal Francis$$Hfree_for_read</backlink></links><search><creatorcontrib>Hegde, R.M.</creatorcontrib><creatorcontrib>Murthy, H.A.</creatorcontrib><creatorcontrib>Gadde, V.R.R.</creatorcontrib><title>Significance of the Modified Group Delay Feature in Speech Recognition</title><title>IEEE transactions on audio, speech, and language processing</title><addtitle>TASL</addtitle><description>Spectral representation of speech is complete when both the Fourier transform magnitude and phase spectra are specified. In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech perception has been realized, few attempts have been made to extract features from it. This is primarily because the resonances of the speech signal which manifest as transitions in the phase spectrum are completely masked by the wrapping of the phase spectrum. Hence, an alternative to processing the Fourier transform phase, for extracting speech features, is to process the group delay function which can be directly computed from the speech signal. The group delay function has been used in earlier efforts, to extract pitch and formant information from the speech signal. In all these efforts, no attempt was made to extract features from the speech signal and use them for speech recognition applications. This is primarily because the group delay function fails to capture the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects. In this paper, the group delay function is modified to overcome these effects. Cepstral features are extracted from the modified group delay function and are called the modified group delay feature (MODGDF). The MODGDF is used for three speech recognition tasks namely, speaker, language, and continuous-speech recognition. Based on the results of feature and performance evaluation, the significance of the MODGDF as a new feature for speech recognition is discussed</description><subject>Applied sciences</subject><subject>Class separability</subject><subject>Data mining</subject><subject>Delay effects</subject><subject>Exact sciences and technology</subject><subject>Feature extraction</subject><subject>feature selection</subject><subject>Fourier transforms</subject><subject>Gaussian mixture models (GMMs)</subject><subject>Group delay</subject><subject>group delay function</subject><subject>hidden Markov models (HMMs)</subject><subject>Information, signal and communications theory</subject><subject>Mathematical analysis</subject><subject>Mathematical models</subject><subject>Miscellaneous</subject><subject>Pattern recognition</subject><subject>phase spectrum</subject><subject>Phase transformations</subject><subject>Resonance</subject><subject>robustness</subject><subject>Signal and communications theory</subject><subject>Signal processing</subject><subject>Signal representation. Spectral analysis</subject><subject>Signal, noise</subject><subject>Spectra</subject><subject>Speech</subject><subject>Speech coding</subject><subject>Speech processing</subject><subject>Speech recognition</subject><subject>Telecommunications and information theory</subject><subject>Voice recognition</subject><subject>Wrapping</subject><issn>1558-7916</issn><issn>2329-9290</issn><issn>1558-7924</issn><issn>2329-9304</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2007</creationdate><recordtype>article</recordtype><sourceid>RIE</sourceid><recordid>eNpdkE1LAzEURYMoWKt7wU0QxFVrPiaTZFmqrUJFsHUd0swbmzKd1GRm0X_vlJYKrl54OffyOAjdUjKklOinxWg-GzJC8qGSuRLqDPWoEGogNcvOT2-aX6KrlNaEZDzPaA9N5v679qV3tnaAQ4mbFeD3UHQrKPA0hnaLn6GyOzwB27QRsK_xfAvgVvgTXOjCjQ_1NboobZXg5jj76Gvyshi_DmYf07fxaDZwGVXNwBIilNY5YVpokWVcalHIZSkc5UAdLQqtuJK8AEaoFEwBMKqZKoTibskc76PHQ-82hp8WUmM2PjmoKltDaJNRUpCcMqk68v4fuQ5trLvjjKYy41xL0kHkALkYUopQmm30Gxt3hhKz12r2Ws1eqzlo7SIPx16bnK3K2Inz6S-nuJT7_j66O3AeAE7fGeFMSsZ_Ab7Yfb4</recordid><startdate>200701</startdate><enddate>200701</enddate><creator>Hegde, R.M.</creator><creator>Murthy, H.A.</creator><creator>Gadde, V.R.R.</creator><general>IEEE</general><general>Institute of Electrical and Electronics Engineers</general><general>The Institute of Electrical and Electronics Engineers, Inc. (IEEE)</general><scope>97E</scope><scope>RIA</scope><scope>RIE</scope><scope>IQODW</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SC</scope><scope>8FD</scope><scope>JQ2</scope><scope>L7M</scope><scope>L~C</scope><scope>L~D</scope></search><sort><creationdate>200701</creationdate><title>Significance of the Modified Group Delay Feature in Speech Recognition</title><author>Hegde, R.M. ; Murthy, H.A. ; Gadde, V.R.R.</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c418t-a0058996029595443795d7bf5c13e1c1dd983873de2017528ee21928d583cb2c3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2007</creationdate><topic>Applied sciences</topic><topic>Class separability</topic><topic>Data mining</topic><topic>Delay effects</topic><topic>Exact sciences and technology</topic><topic>Feature extraction</topic><topic>feature selection</topic><topic>Fourier transforms</topic><topic>Gaussian mixture models (GMMs)</topic><topic>Group delay</topic><topic>group delay function</topic><topic>hidden Markov models (HMMs)</topic><topic>Information, signal and communications theory</topic><topic>Mathematical analysis</topic><topic>Mathematical models</topic><topic>Miscellaneous</topic><topic>Pattern recognition</topic><topic>phase spectrum</topic><topic>Phase transformations</topic><topic>Resonance</topic><topic>robustness</topic><topic>Signal and communications theory</topic><topic>Signal processing</topic><topic>Signal representation. Spectral analysis</topic><topic>Signal, noise</topic><topic>Spectra</topic><topic>Speech</topic><topic>Speech coding</topic><topic>Speech processing</topic><topic>Speech recognition</topic><topic>Telecommunications and information theory</topic><topic>Voice recognition</topic><topic>Wrapping</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hegde, R.M.</creatorcontrib><creatorcontrib>Murthy, H.A.</creatorcontrib><creatorcontrib>Gadde, V.R.R.</creatorcontrib><collection>IEEE All-Society Periodicals Package (ASPP) 2005-present</collection><collection>IEEE All-Society Periodicals Package (ASPP) 1998-Present</collection><collection>IEEE Electronic Library (IEL)</collection><collection>Pascal-Francis</collection><collection>CrossRef</collection><collection>Computer and Information Systems Abstracts</collection><collection>Technology Research Database</collection><collection>ProQuest Computer Science Collection</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Computer and Information Systems Abstracts Academic</collection><collection>Computer and Information Systems Abstracts Professional</collection><jtitle>IEEE transactions on audio, speech, and language processing</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext_linktorsrc</fulltext></delivery><addata><au>Hegde, R.M.</au><au>Murthy, H.A.</au><au>Gadde, V.R.R.</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Significance of the Modified Group Delay Feature in Speech Recognition</atitle><jtitle>IEEE transactions on audio, speech, and language processing</jtitle><stitle>TASL</stitle><date>2007-01</date><risdate>2007</risdate><volume>15</volume><issue>1</issue><spage>190</spage><epage>202</epage><pages>190-202</pages><issn>1558-7916</issn><issn>2329-9290</issn><eissn>1558-7924</eissn><eissn>2329-9304</eissn><coden>ITASD8</coden><abstract>Spectral representation of speech is complete when both the Fourier transform magnitude and phase spectra are specified. In conventional speech recognition systems, features are generally derived from the short-time magnitude spectrum. Although the importance of Fourier transform phase in speech perception has been realized, few attempts have been made to extract features from it. This is primarily because the resonances of the speech signal which manifest as transitions in the phase spectrum are completely masked by the wrapping of the phase spectrum. Hence, an alternative to processing the Fourier transform phase, for extracting speech features, is to process the group delay function which can be directly computed from the speech signal. The group delay function has been used in earlier efforts, to extract pitch and formant information from the speech signal. In all these efforts, no attempt was made to extract features from the speech signal and use them for speech recognition applications. This is primarily because the group delay function fails to capture the short-time spectral structure of speech owing to zeros that are close to the unit circle in the z-plane and also due to pitch periodicity effects. In this paper, the group delay function is modified to overcome these effects. Cepstral features are extracted from the modified group delay function and are called the modified group delay feature (MODGDF). The MODGDF is used for three speech recognition tasks namely, speaker, language, and continuous-speech recognition. Based on the results of feature and performance evaluation, the significance of the MODGDF as a new feature for speech recognition is discussed</abstract><cop>Piscataway, NJ</cop><pub>IEEE</pub><doi>10.1109/TASL.2006.876858</doi><tpages>13</tpages></addata></record> |
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subjects | Applied sciences Class separability Data mining Delay effects Exact sciences and technology Feature extraction feature selection Fourier transforms Gaussian mixture models (GMMs) Group delay group delay function hidden Markov models (HMMs) Information, signal and communications theory Mathematical analysis Mathematical models Miscellaneous Pattern recognition phase spectrum Phase transformations Resonance robustness Signal and communications theory Signal processing Signal representation. Spectral analysis Signal, noise Spectra Speech Speech coding Speech processing Speech recognition Telecommunications and information theory Voice recognition Wrapping |
title | Significance of the Modified Group Delay Feature in Speech Recognition |
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