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Discriminative Phoneme Sequences Extraction for Non-Native Speaker's Origin Classification

Identifieur interne : 004A01 ( Main/Exploration ); précédent : 004A00; suivant : 004A02

Discriminative Phoneme Sequences Extraction for Non-Native Speaker's Origin Classification

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

Source :

RBID : Hal:inria-00184553

Descripteurs français

Abstract

In this paper we present an automated method for the classification of the origin of non-native speakers. The origin of non-native speakers could be identified by a human listener based on the detection of typical pronunciations for each nationality. Thus we suppose the existence of several phoneme sequences that might allow the classification of the origin of non-native speakers. Our new method is based on the extraction of discriminative sequences of phonemes from a non-native English speech database. These sequences are used to construct a probabilistic classifier for the speakers' origin. The existence of discriminative phone sequences in non-native speech is a significant result of this work. The system that we have developed achieved a significant correct classification rate of 96.3% and a significant error reduction compared to some other tested techniques.

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Le document en format XML

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<div type="abstract" xml:lang="en">In this paper we present an automated method for the classification of the origin of non-native speakers. The origin of non-native speakers could be identified by a human listener based on the detection of typical pronunciations for each nationality. Thus we suppose the existence of several phoneme sequences that might allow the classification of the origin of non-native speakers. Our new method is based on the extraction of discriminative sequences of phonemes from a non-native English speech database. These sequences are used to construct a probabilistic classifier for the speakers' origin. The existence of discriminative phone sequences in non-native speech is a significant result of this work. The system that we have developed achieved a significant correct classification rate of 96.3% and a significant error reduction compared to some other tested techniques.</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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