Dynamic Gaussian Selection Technique for Speeding Up HMM-Based Continuous Speech Recognition
Identifieur interne : 001D58 ( Hal/Curation ); précédent : 001D57; suivant : 001D59Dynamic Gaussian Selection Technique for Speeding Up HMM-Based Continuous Speech Recognition
Auteurs : Jun Cai [France] ; Ghazi Bouselmi [France] ; Dominique Fohr [France] ; Yves Laprie [France]Source :
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Abstract
A fast likelihood computation approach called dynamic Gaussian selection (DGS) is proposed for HMM-based continuous speech recognition. DGS approach is a one-pass search technique which generates a dynamic shortlist of Gaussians for each state during the procedure of likelihood computation. The shortlist consists of the Gaussians which make prominent contribution to the likelihood. In principle, DGS is an extension of the technique of Partial Distance Elimination, and it requires almost no additional memory for the storage of Gaussian shortlists. DGS algorithm has been implemented by modifying the likelihood computation module in HTK 3.4 system. Results from experiments on TIMIT and HIWIRE corpora indicate that this approach can speed up the likelihood computation significantly without introducing apparent additional recognition error.
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type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="en"><term>Gaussian selection</term><term>fast likelihood computation</term><term>hidden Markov models</term><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">A fast likelihood computation approach called dynamic Gaussian selection (DGS) is proposed for HMM-based continuous speech recognition. DGS approach is a one-pass search technique which generates a dynamic shortlist of Gaussians for each state during the procedure of likelihood computation. The shortlist consists of the Gaussians which make prominent contribution to the likelihood. In principle, DGS is an extension of the technique of Partial Distance Elimination, and it requires almost no additional memory for the storage of Gaussian shortlists. DGS algorithm has been implemented by modifying the likelihood computation module in HTK 3.4 system. Results from experiments on TIMIT and HIWIRE corpora indicate that this approach can speed up the likelihood computation significantly without introducing apparent additional recognition error.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Dynamic Gaussian Selection Technique for Speeding Up HMM-Based Continuous Speech Recognition</title><author role="aut"><persName><forename type="first">Jun</forename><surname>Cai</surname></persName><email>Jun.Cai@loria.fr</email><idno type="halauthor">358024</idno><orgName ref="#struct-366170"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Ghazi</forename><surname>Bouselmi</surname></persName><email>bousselm@loria.fr</email><idno type="halauthor">141749</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Dominique</forename><surname>Fohr</surname></persName><email>dominique.fohr@loria.fr</email><idno type="halauthor">66027</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Yves</forename><surname>Laprie</surname></persName><email>Yves.Laprie@loria.fr</email><idno type="idhal">yves-laprie</idno><idno type="halauthor">58846</idno><affiliation ref="#struct-2359"></affiliation></author><editor role="depositor"><persName><forename>Dominique</forename><surname>Fohr</surname></persName><email>dominique.fohr@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2008-10-09 11:46:27</date><date type="whenModified">2016-05-18 08:57:56</date><date type="whenReleased">2008-10-09 11:46:27</date><date type="whenProduced">2008-04</date></edition><respStmt><resp>contributor</resp><name key="103934"><persName><forename>Dominique</forename><surname>Fohr</surname></persName><email>dominique.fohr@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00327703</idno><idno type="halUri">https://hal.inria.fr/inria-00327703</idno><idno type="halBibtex">cai:inria-00327703</idno><idno type="halRefHtml">ICASSP, Apr 2008, Las Vegas, United States. 2008</idno><idno type="halRef">ICASSP, Apr 2008, Las Vegas, United States. 2008</idno></publicationStmt><seriesStmt><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</idno><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="INPL">Institut National Polytechnique de Lorraine</idno><idno type="stamp" n="LORIA2">Publications du LORIA</idno><idno type="stamp" n="INRIA-NANCY-GRAND-EST">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</idno><idno type="stamp" n="LORIA-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="INRIA2">INRIA 2</idno><idno type="stamp" n="INRIA-LORRAINE">INRIA Nancy - Grand Est</idno><idno type="stamp" n="LABO-LORIA-SET" p="LORIA">LABO-LORIA-SET</idno><idno type="stamp" n="UNIV-LORRAINE">Université de Lorraine</idno></seriesStmt><notesStmt><note type="audience" n="2">International</note><note type="invited" n="0">No</note><note type="popular" n="0">No</note><note type="peer" n="1">Yes</note><note type="proceedings" n="1">Yes</note></notesStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Dynamic Gaussian Selection Technique for Speeding Up HMM-Based Continuous Speech Recognition</title><author role="aut"><persName><forename type="first">Jun</forename><surname>Cai</surname></persName><email>Jun.Cai@loria.fr</email><idno type="halAuthorId">358024</idno><orgName ref="#struct-366170"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Ghazi</forename><surname>Bouselmi</surname></persName><email>bousselm@loria.fr</email><idno type="halAuthorId">141749</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Dominique</forename><surname>Fohr</surname></persName><email>dominique.fohr@loria.fr</email><idno type="halAuthorId">66027</idno><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Yves</forename><surname>Laprie</surname></persName><email>Yves.Laprie@loria.fr</email><idno type="idHal">yves-laprie</idno><idno type="halAuthorId">58846</idno><affiliation ref="#struct-2359"></affiliation></author></analytic><monogr><meeting><title>ICASSP</title><date type="start">2008-04</date><settlement>Las Vegas</settlement><country key="US">United States</country></meeting><imprint><date type="datePub">2008</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="en">Gaussian selection</term><term xml:lang="en">fast likelihood computation</term><term xml:lang="en">hidden Markov models</term><term xml:lang="en">speech recognition</term></keywords><classCode scheme="halDomain" n="info.info-ts">Computer Science [cs]/Signal and Image Processing</classCode><classCode scheme="halDomain" n="spi.signal">Engineering Sciences [physics]/Signal and Image processing</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">A fast likelihood computation approach called dynamic Gaussian selection (DGS) is proposed for HMM-based continuous speech recognition. DGS approach is a one-pass search technique which generates a dynamic shortlist of Gaussians for each state during the procedure of likelihood computation. The shortlist consists of the Gaussians which make prominent contribution to the likelihood. In principle, DGS is an extension of the technique of Partial Distance Elimination, and it requires almost no additional memory for the storage of Gaussian shortlists. DGS algorithm has been implemented by modifying the likelihood computation module in HTK 3.4 system. Results from experiments on TIMIT and HIWIRE corpora indicate that this approach can speed up the likelihood computation significantly without introducing apparent additional recognition error.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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