T. Avec and . Gmm, 91% LM-dGMM meilleure itération (it. 1) 8.40% dernì ere itération (it. 30) 12.80% (a) ModèlesModèlesà 256 gaussiennes

E. Système and T. Sans, 74% LM-dGMM meilleure itération (it. 1) 9.66% dernì ere itération (it. 9) 10, p.47

T. Avec and . Gmm, 90% LM-dGMM meilleure itération (it. 1) 8, 85% dernì ere itération (it. 9) 9.66% (b) ModèlesModèlesà 512 gaussiennes

. Tab, 22: EER de systèmes GMM et LM-dGMM, avec et sans une T-normalisation des scores

. Utilisation-de-la-compensation-;-´-etude-comparative-comme-observé and . Fauve, le puissant formalisme SFA permet d'´ eliminer efficacement la variabilité inter-sessions, sans faire appeì a la T-normalisation des scores. Nous n'allons donc plus utiliser cette technique de normalisation dans les prochaines expériences, Les tableaux Tab. 4.23 et Tab. 4.24 rappellent les performances du système-GMM et du système-LM-dGMM sans compensation, et affichent ceux des système-GMM-SFA et système-LM-dGMM-SFA o` u la compensation est utilisée, avec respectivement M = 256, 2007.

. Au-cours-de-ce-travail-de-thèse, nous avons proposé d'utiliser une nouvelle approche discriminante pour la reconnaissance automatique du locuteur qui consistè a utiliser des modèles GMMàGMMà grande marge, appelés LM-dGMM. Nos modèles reposent sur une récente approche discriminante pour la séparation multi-classes, qui a ´ eté appliquée en reconnaissance de la parole ; les modèles LM-GMM. Les LM-dGMM sont définis par un vecteur centro¨?decentro¨?de, une matrice de covariance diagonale et un offset

L. En-comparaison-avec-les-modèles and . Originaux, l'apprentissage des LM-dGMM est beaucoup moins complexe : l'algorithme d'apprentissage simplifié est plus rapide et moins demandeur de mémoire, De plus, ces modèles donnent de bien meilleurs résultats que les modèles LM-GMM originaux

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