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Amélioration des Performances des Systèmes Automatiques de Reconnaissance de la Parole pour la Parole Non Native

Identifieur interne : 004926 ( Main/Exploration ); précédent : 004925; suivant : 004927

Amélioration des Performances des Systèmes Automatiques de Reconnaissance de la Parole pour la Parole Non Native

Auteurs : Ghazi Bouselmi [France] ; Dominique Fohr [France] ; Irina Illina [France] ; Jean-Paul Haton [France]

Source :

RBID : Hal:inria-00184565

Descripteurs français

Abstract

In this article, we present an approach for non native automatic speech recognition (ASR). We propose two methods to adapt existing ASR systems to the non-native accents. The first method is based on the modification of acoustic models through integration of acoustic models from the mother tong. The phonemes of the target language are pronounced in a similar manner to the native language of speakers. We propose to combine the models of confused phonemes so that the ASR system could recognize both concurrent pronounciations. The second method we propose is a refinment of the pronounciation error detection through the introduction of graphemic constraints. Indeed, non native speakers may rely on the writing of words in their uttering. Thus, the pronounctiation errors might depend on the characters composing the words. The average error rate reduction that we observed is (22.5%) relative for the sentence error rate, and 34.5% (relative) in word error rate.

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Le document en format XML

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<keywords scheme="mix" xml:lang="fr">
<term>Reconnaissance de la parole non native</term>
<term>confusion phonétique</term>
<term>contraintes graphèmiques</term>
<term>modification de HMM</term>
</keywords>
</textClass>
</profileDesc>
</teiHeader>
<front>
<div type="abstract" xml:lang="en">In this article, we present an approach for non native automatic speech recognition (ASR). We propose two methods to adapt existing ASR systems to the non-native accents. The first method is based on the modification of acoustic models through integration of acoustic models from the mother tong. The phonemes of the target language are pronounced in a similar manner to the native language of speakers. We propose to combine the models of confused phonemes so that the ASR system could recognize both concurrent pronounciations. The second method we propose is a refinment of the pronounciation error detection through the introduction of graphemic constraints. Indeed, non native speakers may rely on the writing of words in their uttering. Thus, the pronounctiation errors might depend on the characters composing the words. The average error rate reduction that we observed is (22.5%) relative for the sentence error rate, and 34.5% (relative) in word error rate.</div>
</front>
</TEI>
<affiliations>
<list>
<country>
<li>France</li>
</country>
<region>
<li>Grand Est</li>
<li>Lorraine (région)</li>
</region>
<settlement>
<li>Nancy</li>
</settlement>
<orgName>
<li>Institut national polytechnique de Lorraine</li>
<li>Université Nancy 2</li>
<li>Université de Lorraine</li>
</orgName>
</list>
<tree>
<country name="France">
<region name="Grand Est">
<name sortKey="Bouselmi, Ghazi" sort="Bouselmi, Ghazi" uniqKey="Bouselmi G" first="Ghazi" last="Bouselmi">Ghazi Bouselmi</name>
</region>
<name sortKey="Fohr, Dominique" sort="Fohr, Dominique" uniqKey="Fohr D" first="Dominique" last="Fohr">Dominique Fohr</name>
<name sortKey="Haton, Jean Paul" sort="Haton, Jean Paul" uniqKey="Haton J" first="Jean-Paul" last="Haton">Jean-Paul Haton</name>
<name sortKey="Illina, Irina" sort="Illina, Irina" uniqKey="Illina I" first="Irina" last="Illina">Irina Illina</name>
</country>
</tree>
</affiliations>
</record>

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