Exploiting models intrinsic robustness for noisy speech recognition
Identifieur interne : 004E54 ( Hal/Checkpoint ); précédent : 004E53; suivant : 004E55Exploiting models intrinsic robustness for noisy speech recognition
Auteurs : Christophe Cerisara [France] ; Dominique Fohr [France] ; Odile Mella [France] ; Irina Illina [France]Source :
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Abstract
We propose in this paper an original approach to build masks in the framework of missing data recognition. The proposed soft masks are estimated from the models themselves, and not from the test signal as it is usually the case. They represent the intrinsic robustness of model's log-spectral coefficients. The method is validated with cepstral models, on two synthetic and two real-life noises, at different signal-to-noise ratios. We further discuss how such masks can be combined with other signal-based masks and noise compensation techniques.
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nuts="2">Grand Est</region><region type="old region" nuts="2">Lorraine (région)</region></placeName><orgName type="university">Institut national polytechnique de Lorraine</orgName><orgName type="institution" wicri:auto="newGroup">Université de Lorraine</orgName></affiliation></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="mix" xml:lang="fr"><term>données manquantes</term><term>missing data</term><term>reconnaissance de la parole</term><term>robustesse</term><term>robustness</term><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We propose in this paper an original approach to build masks in the framework of missing data recognition. The proposed soft masks are estimated from the models themselves, and not from the test signal as it is usually the case. They represent the intrinsic robustness of model's log-spectral coefficients. The method is validated with cepstral models, on two synthetic and two real-life noises, at different signal-to-noise ratios. We further discuss how such masks can be combined with other signal-based masks and noise compensation techniques.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">Exploiting models intrinsic robustness for noisy speech recognition</title><author role="aut"><persName><forename type="first">Christophe</forename><surname>Cerisara</surname></persName><email>christophe.cerisara@loria.fr</email><idno type="idhal">christophe-cerisara</idno><idno type="halauthor">130612</idno><orgName ref="#struct-441569"></orgName><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">130115</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Odile</forename><surname>Mella</surname></persName><email></email><idno type="halauthor">130334</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Irina</forename><surname>Illina</surname></persName><email></email><idno type="halauthor">181677</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2359"></affiliation></author><editor role="depositor"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></editor></titleStmt><editionStmt><edition n="v1" type="current"><date type="whenSubmitted">2006-09-26 10:03:33</date><date type="whenModified">2016-05-18 08:59:43</date><date type="whenReleased">2006-09-28 15:22:46</date><date type="whenProduced">2004</date><date type="whenEndEmbargoed">2015-02-10</date><ref type="file" target="https://hal.inria.fr/inria-00099890/document"><date notBefore="2015-02-10"></date></ref><ref type="file" n="1" target="https://hal.inria.fr/inria-00099890/file/A04-R-270.pdf"><date notBefore="2015-02-10"></date></ref></edition><respStmt><resp>contributor</resp><name key="108626"><persName><forename>Publications</forename><surname>Loria</surname></persName><email>publications@loria.fr</email></name></respStmt></editionStmt><publicationStmt><distributor>CCSD</distributor><idno type="halId">inria-00099890</idno><idno type="halUri">https://hal.inria.fr/inria-00099890</idno><idno type="halBibtex">cerisara:inria-00099890</idno><idno type="halRefHtml">8th International Conference on Spoken Language Processing - ICSLP'2004, 2004, Jeju, Corée du Sud, 4 p, 2004</idno><idno type="halRef">8th International Conference on Spoken Language Processing - ICSLP'2004, 2004, Jeju, Corée du Sud, 4 p, 2004</idno></publicationStmt><seriesStmt><idno type="stamp" n="INRIA">INRIA - Institut National de Recherche en Informatique et en Automatique</idno><idno type="stamp" n="CNRS">CNRS - Centre national de la recherche scientifique</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="commentary">Colloque avec actes et comité de lecture. internationale.</note><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">Exploiting models intrinsic robustness for noisy speech recognition</title><author role="aut"><persName><forename type="first">Christophe</forename><surname>Cerisara</surname></persName><email>christophe.cerisara@loria.fr</email><idno type="idHal">christophe-cerisara</idno><idno type="halAuthorId">130612</idno><orgName ref="#struct-441569"></orgName><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">130115</idno><orgName ref="#struct-441569"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Odile</forename><surname>Mella</surname></persName><idno type="halAuthorId">130334</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Irina</forename><surname>Illina</surname></persName><idno type="halAuthorId">181677</idno><orgName ref="#struct-300292"></orgName><affiliation ref="#struct-2359"></affiliation></author></analytic><monogr><idno type="localRef">A04-R-270 || cerisara04b</idno><meeting><title>8th International Conference on Spoken Language Processing - ICSLP'2004</title><date type="start">2004</date><settlement>Jeju, Corée du Sud</settlement></meeting><imprint><biblScope unit="pp">4 p</biblScope><date type="datePub">2004</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">reconnaissance de la parole</term><term xml:lang="fr">missing data</term><term xml:lang="fr">speech recognition</term><term xml:lang="fr">robustness</term><term xml:lang="fr">robustesse</term><term xml:lang="fr">données manquantes</term></keywords><classCode scheme="halDomain" n="info.info-oh">Computer Science [cs]/Other [cs.OH]</classCode><classCode scheme="halTypology" n="COMM">Conference papers</classCode></textClass><abstract xml:lang="en">We propose in this paper an original approach to build masks in the framework of missing data recognition. The proposed soft masks are estimated from the models themselves, and not from the test signal as it is usually the case. They represent the intrinsic robustness of model's log-spectral coefficients. The method is validated with cepstral models, on two synthetic and two real-life noises, at different signal-to-noise ratios. We further discuss how such masks can be combined with other signal-based masks and noise compensation techniques.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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