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On MMI Learning of Gaussian Mixture for Speaker Models

Identifieur interne : 00CB75 ( Main/Merge ); précédent : 00CB74; suivant : 00CB76

On MMI Learning of Gaussian Mixture for Speaker Models

Auteurs : H. Li ; Jean-Paul Haton [France] ; Y. Gong

Source :

RBID : CRIN:li95a

English descriptors

Abstract

In this paper, a general framework of maximum mutual information (MMI) learning of mixture densities is developed based on the discriminative learning strategy, within which a family of probabilistic classifiers can be trained. Two case studies are presented concerning class dependent Gaussian mixture model (GMM) and its extension to the case of tying kernel across classes. The related learning algorithms are derived. In the speaker recognition experiments, each speaker is represented by a GMM. The algorithms train the models aiming at minimum error rate. A normalized distance is also introduced to speaker verification. Five algorithms are evaluated for comparison and 100========percnt; of speaker verification rate is obtained on a database of 200 French speakers.

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CRIN:li95a

Le document en format XML

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<region type="region" nuts="2">Grand Est</region>
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<orgName type="laboratoire" n="5">Laboratoire lorrain de recherche en informatique et ses applications</orgName>
<orgName type="university">Université de Lorraine</orgName>
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<orgName type="institution">Institut national de recherche en informatique et en automatique</orgName>
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<div type="abstract" xml:lang="en" wicri:score="2024">In this paper, a general framework of maximum mutual information (MMI) learning of mixture densities is developed based on the discriminative learning strategy, within which a family of probabilistic classifiers can be trained. Two case studies are presented concerning class dependent Gaussian mixture model (GMM) and its extension to the case of tying kernel across classes. The related learning algorithms are derived. In the speaker recognition experiments, each speaker is represented by a GMM. The algorithms train the models aiming at minimum error rate. A normalized distance is also introduced to speaker verification. Five algorithms are evaluated for comparison and 100========percnt; of speaker verification rate is obtained on a database of 200 French speakers.</div>
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{{Explor lien
   |wiki=    Wicri/Lorraine
   |area=    InforLorV4
   |flux=    Main
   |étape=   Merge
   |type=    RBID
   |clé=     CRIN:li95a
   |texte=   On MMI Learning of Gaussian Mixture for Speaker Models
}}
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