Réseaux Bayésiens Dynamiques pour la Reconnaissance Multi-Bandes de la Parole
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Auteurs : Khalid Daoudi ; Dominique Fohr ; Christophe AntoineSource :
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Abstract
This paper presents a new approach to multi-band automatic speech recognition which has the advantage to overcome many limitations of classical muti-band systems. The principle of this new approach is to build a speech model in the time-frequency domain using the formalism of Bayesian networks. Contrarily to classical multi-band modeling, this formalism leads to a probabilistic speech model which allows communications between the different sub-bands and, consequently, no recombination step is required in recognition. We develop efficient learning and decoding algorithms and present illustrative experiments on a connected digit recognition task. The experiments show that the Bayesian network's approach is very promising in the field of noisy speech recognition.
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<record><TEI><teiHeader><fileDesc><titleStmt><title xml:lang="fr" wicri:score="-106">Réseaux Bayésiens Dynamiques pour la Reconnaissance Multi-Bandes de la Parole</title></titleStmt><publicationStmt><idno type="RBID">CRIN:daoudi02d</idno><date when="2002" year="2002">2002</date><idno type="wicri:Area/Crin/Corpus">003453</idno><idno type="wicri:Area/Crin/Curation">003453</idno><idno type="wicri:explorRef" wicri:stream="Crin" wicri:step="Curation">003453</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="fr">Réseaux Bayésiens Dynamiques pour la Reconnaissance Multi-Bandes de la Parole</title><author><name sortKey="Daoudi, Khalid" sort="Daoudi, Khalid" uniqKey="Daoudi K" first="Khalid" last="Daoudi">Khalid Daoudi</name></author><author><name sortKey="Fohr, Dominique" sort="Fohr, Dominique" uniqKey="Fohr D" first="Dominique" last="Fohr">Dominique Fohr</name></author><author><name sortKey="Antoine, Christophe" sort="Antoine, Christophe" uniqKey="Antoine C" first="Christophe" last="Antoine">Christophe Antoine</name></author></analytic></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>bayesian networks</term><term>speech recognition</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en" wicri:score="3267">This paper presents a new approach to multi-band automatic speech recognition which has the advantage to overcome many limitations of classical muti-band systems. The principle of this new approach is to build a speech model in the time-frequency domain using the formalism of Bayesian networks. Contrarily to classical multi-band modeling, this formalism leads to a probabilistic speech model which allows communications between the different sub-bands and, consequently, no recombination step is required in recognition. We develop efficient learning and decoding algorithms and present illustrative experiments on a connected digit recognition task. The experiments show that the Bayesian network's approach is very promising in the field of noisy speech recognition.</div></front></TEI><BibTex type="inproceedings"><ref>daoudi02d</ref><crinnumber>A02-R-257</crinnumber><category>3</category><equipe>PAROLE</equipe><author><e>Daoudi, Khalid</e><e>Fohr, Dominique</e><e>Antoine, Christophe</e></author><title>Réseaux Bayésiens Dynamiques pour la Reconnaissance Multi-Bandes de la Parole</title><booktitle>{XXIVe Journées d'Etudes sur la Parole - JEP'2002, Nancy, France}</booktitle><year>2002</year><month>Jun</month><organization>Equipe Parole - LORIA</organization><url>http://www.loria.fr/publications/2002/A02-R-257/A02-R-257.ps</url><keywords><e>speech recognition</e><e>bayesian networks</e></keywords><abstract>This paper presents a new approach to multi-band automatic speech recognition which has the advantage to overcome many limitations of classical muti-band systems. The principle of this new approach is to build a speech model in the time-frequency domain using the formalism of Bayesian networks. Contrarily to classical multi-band modeling, this formalism leads to a probabilistic speech model which allows communications between the different sub-bands and, consequently, no recombination step is required in recognition. We develop efficient learning and decoding algorithms and present illustrative experiments on a connected digit recognition task. The experiments show that the Bayesian network's approach is very promising in the field of noisy speech recognition.</abstract></BibTex></record>Pour manipuler ce document sous Unix (Dilib)
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