THE INFINITE GAUSSIAN MODELS: AN APPLICATION TO SPEAKER IDENTIFICATION

  • Souad FRIHA Institut de Génie Electrique, University of Tebessa
  • Nora MANSOURI Laboratoire d'Automatique et de Robotique, University of Mentouri, Route Ain EL Bey, Constantine
  • Abdelmalik TALEB AHMED University of Valenciennes et du Hainaut Cambrésis LAMIH UMR CNRS-UVHC 8530, Univ. de Valenciennes et du Hainaut Cambrésis, Le mont Houy 59313 Valenciennes Cedex 9

Résumé

When modeling speech with traditional Gaussian Mixture Models (GMM) a major problem is that one need to fix a priori the
number of GMMs. Using the infinite version of GMMs allows to overcome this problem. This is based on considering a
Dirichlet process with a Bayesian inference via Gibbs sampling rather than the traditional EM inference. The paper
investigates the usefulness of the infinite Gaussian modeling using the state of the art SVM classifiers. We consider the
particular case of the speaker identification under limited data condition that is very short speech sequences. Basically,
recognition rates of 100% are achieved after only 5 iterations using training and test samples less than 1 second. Experiments
are carried out over NIST SRE 2000 corpus.

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The infinite Gaussian models an application to speaker identification
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Comment citer
FRIHA, Souad; MANSOURI, Nora; TALEB AHMED, Abdelmalik. THE INFINITE GAUSSIAN MODELS: AN APPLICATION TO SPEAKER IDENTIFICATION. Courrier du Savoir, [S.l.], v. 12, mai 2014. ISSN 1112-3338. Disponible à l'adresse : >http://revues.univ-biskra.dz/index.php/cds/article/view/458>. Date de consultation : 12 jui. 2020
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