Une nouvelle architecture de compensation du bruit pour la reconnaissance robuste de la parole
Identifieur interne : 006864 ( Hal/Curation ); précédent : 006863; suivant : 006865Une nouvelle architecture de compensation du bruit pour la reconnaissance robuste de la parole
Auteurs : Khalid Daoudi [France] ; Murat Deviren [France]Source :
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Abstract
We present a novel noise compensation architecture which makes no assumptions on how the noise sources alter the speech data and which do not rely on clean speech models. Rather, this new architecture makes the (realistic) assumption that speech databases recorded under different background noise conditions are available. Its main principle is to process individually each database and to construct a parametric representation which describes the variation of acoustic models w.r.t. noise models. This representation is then used during recognition to estimate the acoustic models in the new environment. We evaluate the performance of this new compensation scheme on a connected digits recognition task and show that it can perform significantly better than multi-conditions training, which is the most widely used technique in these kind of scenarios.
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Lorraine</orgName><placeName><settlement type="city">Nancy</settlement><region type="region" 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>noise robustness</term><term>reconnaissance de la parole</term><term>robustess au bruit</term><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We present a novel noise compensation architecture which makes no assumptions on how the noise sources alter the speech data and which do not rely on clean speech models. Rather, this new architecture makes the (realistic) assumption that speech databases recorded under different background noise conditions are available. Its main principle is to process individually each database and to construct a parametric representation which describes the variation of acoustic models w.r.t. noise models. This representation is then used during recognition to estimate the acoustic models in the new environment. We evaluate the performance of this new compensation scheme on a connected digits recognition task and show that it can perform significantly better than multi-conditions training, which is the most widely used technique in these kind of scenarios.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="fr">Une nouvelle architecture de compensation du bruit pour la reconnaissance robuste de la parole</title><author role="aut"><persName><forename type="first">Khalid</forename><surname>Daoudi</surname></persName><email>daoudi@loria.fr</email><idno type="halauthor">130237</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Murat</forename><surname>Deviren</surname></persName><email>deviren@loria.fr</email><idno type="halauthor">131002</idno><orgName ref="#struct-364475"></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">2006-10-19 09:09:12</date><date type="whenModified">2016-05-18 08:53:48</date><date type="whenReleased">2006-10-20 15:32:31</date><date type="whenProduced">2004</date><date type="whenEndEmbargoed">2015-02-10</date><ref type="file" target="https://hal.inria.fr/inria-00107784/document"><date notBefore="2015-02-10"></date></ref><ref type="file" n="1" target="https://hal.inria.fr/inria-00107784/file/A04-R-100.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-00107784</idno><idno type="halUri">https://hal.inria.fr/inria-00107784</idno><idno type="halBibtex">daoudi:inria-00107784</idno><idno type="halRefHtml">XXVes Journées d'Etudes sur la Parole - JEP-TALN-RECITAL 2004, 2004, Fès, Maroc, 4 p, 2004</idno><idno type="halRef">XXVes Journées d'Etudes sur la Parole - JEP-TALN-RECITAL 2004, 2004, Fès, Maroc, 4 p, 2004</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="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="fr">Une nouvelle architecture de compensation du bruit pour la reconnaissance robuste de la parole</title><author role="aut"><persName><forename type="first">Khalid</forename><surname>Daoudi</surname></persName><email>daoudi@loria.fr</email><idno type="halAuthorId">130237</idno><orgName ref="#struct-300009"></orgName><affiliation ref="#struct-2359"></affiliation></author><author role="aut"><persName><forename type="first">Murat</forename><surname>Deviren</surname></persName><email>deviren@loria.fr</email><idno type="halAuthorId">131002</idno><orgName ref="#struct-364475"></orgName><affiliation ref="#struct-2359"></affiliation></author></analytic><monogr><idno type="localRef">A04-R-100 || daoudi04a</idno><meeting><title>XXVes Journées d'Etudes sur la Parole - JEP-TALN-RECITAL 2004</title><date type="start">2004</date><settlement>Fès, Maroc</settlement></meeting><imprint><biblScope unit="pp">4 p</biblScope><date type="datePub">2004</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="fr">French</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">reconnaissance de la parole</term><term xml:lang="fr">noise robustness</term><term xml:lang="fr">speech recognition</term><term xml:lang="fr">robustess au bruit</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 present a novel noise compensation architecture which makes no assumptions on how the noise sources alter the speech data and which do not rely on clean speech models. Rather, this new architecture makes the (realistic) assumption that speech databases recorded under different background noise conditions are available. Its main principle is to process individually each database and to construct a parametric representation which describes the variation of acoustic models w.r.t. noise models. This representation is then used during recognition to estimate the acoustic models in the new environment. We evaluate the performance of this new compensation scheme on a connected digits recognition task and show that it can perform significantly better than multi-conditions training, which is the most widely used technique in these kind of scenarios.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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