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On MMI Learning of Gaussian Mixture for Speaker Models

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On MMI Learning of Gaussian Mixture for Speaker Models

Auteurs : H. Li ; J.-P. Haton ; Y. Gong

Source :

RBID : CRIN:li95a

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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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<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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<crinnumber>95-R-263</crinnumber>
<category>3</category>
<equipe>RFIA</equipe>
<author>
<e>Li, H.</e>
<e>Haton, J.-P.</e>
<e>Gong, Y.</e>
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<title>On MMI Learning of Gaussian Mixture for Speaker Models</title>
<booktitle>{Proceedings 4th European Conference on Speech Communication and Technology, Madrid (Spain)}</booktitle>
<year>1995</year>
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<pages>363-366</pages>
<month>sep</month>
<keywords>
<e>speaker recognition</e>
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<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.</abstract>
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