A new supervised-predictive compensation scheme for noisy speech recognition
Identifieur interne : 000793 ( Hal/Curation ); précédent : 000792; suivant : 000794A new supervised-predictive compensation scheme for noisy speech recognition
Auteurs : Khalid Daoudi [France] ; Murat Deviren [France]Source :
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Abstract
We present a new predictive compensation scheme which makes no assumption on how the noise sources alter the speech data and which do not rely on clean speech models. Rather, this new scheme makes the (realistic) assumption that speech databases recorded under different background noise conditions are available. The philosophy of this scheme is to process these databases in order to build a "tool" which will allow it to handle new noise conditions in a robust way. We evaluate the performances 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 techniques 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>acoustic model compensation</term><term>compensation des modèles acoustiques</term><term>noise robustness</term><term>robustesse au bruit</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en">We present a new predictive compensation scheme which makes no assumption on how the noise sources alter the speech data and which do not rely on clean speech models. Rather, this new scheme makes the (realistic) assumption that speech databases recorded under different background noise conditions are available. The philosophy of this scheme is to process these databases in order to build a "tool" which will allow it to handle new noise conditions in a robust way. We evaluate the performances 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 techniques in these kind of scenarios.</div></front></TEI><hal api="V3"><titleStmt><title xml:lang="en">A new supervised-predictive compensation scheme for noisy speech recognition</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">2006-09-26 09:40:57</date><date type="whenModified">2016-05-18 08:59:39</date><date type="whenReleased">2006-09-28 15:22:46</date><date type="whenProduced">2003</date></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-00099754</idno><idno type="halUri">https://hal.inria.fr/inria-00099754</idno><idno type="halBibtex">daoudi:inria-00099754</idno><idno type="halRefHtml">8th European Conference on Speech Communication and Technology - Eurospeech 2003, 2003, Geneva, Switzerland, 4 p, 2003</idno><idno type="halRef">8th European Conference on Speech Communication and Technology - Eurospeech 2003, 2003, Geneva, Switzerland, 4 p, 2003</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-TALC" p="LORIA">Traitement automatique des langues et des connaissances</idno><idno type="stamp" n="LORIA">LORIA - Laboratoire Lorrain de Recherche en Informatique et ses Applications</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. nationale.</note><note type="audience" n="3">National</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">A new supervised-predictive compensation scheme for noisy speech recognition</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">A03-R-530 || daoudi03a</idno><meeting><title>8th European Conference on Speech Communication and Technology - Eurospeech 2003</title><date type="start">2003</date><settlement>Geneva, Switzerland</settlement></meeting><imprint><biblScope unit="pp">4 p</biblScope><date type="datePub">2003</date></imprint></monogr></biblStruct></sourceDesc><profileDesc><langUsage><language ident="en">English</language></langUsage><textClass><keywords scheme="author"><term xml:lang="fr">robustesse au bruit</term><term xml:lang="fr">acoustic model compensation</term><term xml:lang="fr">noise robustness</term><term xml:lang="fr">compensation des modèles acoustiques</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 new predictive compensation scheme which makes no assumption on how the noise sources alter the speech data and which do not rely on clean speech models. Rather, this new scheme makes the (realistic) assumption that speech databases recorded under different background noise conditions are available. The philosophy of this scheme is to process these databases in order to build a "tool" which will allow it to handle new noise conditions in a robust way. We evaluate the performances 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 techniques in these kind of scenarios.</abstract></profileDesc></hal></record>Pour manipuler ce document sous Unix (Dilib)
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