Support Vector Gmms for Speaker Verification

Author

Dehak, Najim ; Chollet, Gerard

Author_Institution

Centre de Recherche Informatique de Montreal, Ecole de Technol. Superieure

fYear

2006

fDate

28-30 June 2006

Firstpage

1

Lastpage

4

Abstract

This article presents a new approach using the discrimination power of support vectors machines (SVM) in combination with Gaussian mixture models (GMM) for automatic speaker verification (ASV). In this combination SVMs are applied in the GMM model space. Each point of this space represents a GMM speaker model. The kernel which is used for the SVM allows the computation of a similarity between GMM models. It was calculated using the Kullback-Leibler (KL) divergence. The results of this new approach show a clear improvement compared to a simple GMM system on the NIST2005 Speaker Recognition Evaluation primary task

Keywords

Gaussian processes; speaker recognition; support vector machines; ASV; GMM; Gaussian mixture model; Kullback-Leibler divergence; NIST2005 Speaker Recognition Evaluation; SVM; automatic speaker verification; discrimination power; support vector machine; Acoustics; Kernel; Loudspeakers; Machine learning; Power system modeling; Space technology; Speaker recognition; Speech; Support vector machines; Testing;

fLanguage

English

Publisher

ieee

Conference_Titel

Speaker and Language Recognition Workshop, 2006. IEEE Odyssey 2006: The

Conference_Location

San Juan

Print_ISBN

1-424400471-1

Electronic_ISBN

1-4244-0472-X

Type

conf

DOI

10.1109/ODYSSEY.2006.248131

Filename

4013548