Combination of SVM and Large Margin GMM modeling for speaker identification

Abstract : Most state-of-the-art speaker recognition systems are partially or completely based on Gaussian mixture models (GMM). GMM have been widely and successfully used in speaker recognition during the last decades. They are traditionally estimated from a world model using the generative criterion of Maximum A Posteriori. In an earlier work, we proposed an efficient algorithm for discriminative learning of GMM with diagonal covariances under a large margin criterion. In this paper, we evaluate the combination of the large margin GMM modeling approach with SVM in the setting of speaker identification. We carry out a full NIST speaker identification task using NIST-SRE'2006 data, in a Symmetrical Factor Analysis compensation scheme. The results show that the two modeling approaches are complementary and that their combination outperforms their single use.
Type de document :
Communication dans un congrès
Eusipco, Sep 2013, Marrakech, Morocco. 2013
Liste complète des métadonnées

Littérature citée [21 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-00908372
Contributeur : Khalid Daoudi <>
Soumis le : vendredi 22 novembre 2013 - 17:16:40
Dernière modification le : jeudi 11 janvier 2018 - 06:21:34
Document(s) archivé(s) le : dimanche 23 février 2014 - 04:31:10

Fichier

eusipco13paper.pdf
Fichiers produits par l'(les) auteur(s)

Identifiants

  • HAL Id : hal-00908372, version 1

Collections

Citation

Reda Jourani, K. Daoudi, Régine André-Obrecht, Driss Aboutajdine. Combination of SVM and Large Margin GMM modeling for speaker identification. Eusipco, Sep 2013, Marrakech, Morocco. 2013. 〈hal-00908372〉

Partager

Métriques

Consultations de la notice

416

Téléchargements de fichiers

332