SVM Based Speaker Verification using a GMM Supervector Kernel and NAP Variability Compensation

Author

Campbell, W.M. ; Sturim, D.E. ; Reynolds, D.A. ; Solomonoff, A.

Author_Institution

MIT Lincoln Lab., Lexington, MA

Volume

1

fYear

2006

fDate

14-19 May 2006

Abstract

Gaussian mixture models with universal backgrounds (UBMs) have become the standard method for speaker recognition. Typically, a speaker model is constructed by MAP adaptation of the means of the UBM. A GMM supervector is constructed by stacking the means of the adapted mixture components. A recent discovery is that latent factor analysis of this GMM supervector is an effective method for variability compensation. We consider this GMM supervector in the context of support vector machines. We construct a support vector machine kernel using the GMM supervector. We show similarities based on this kernel between the method of SVM nuisance attribute projection (NAP) and the recent results in latent factor analysis. Experiments on a NIST SRE 2005 corpus demonstrate the effectiveness of the new technique

Keywords

Gaussian processes; speaker recognition; support vector machines; GMM supervector kernel; Gaussian mixture models; NAP variability compensation; SVM; nuisance attribute projection; speaker recognition; speaker verification; support vector machines; Acoustic signal detection; Kernel; Laboratories; Loudspeakers; Maximum likelihood detection; NIST; Speaker recognition; Stacking; Support vector machine classification; Support vector machines;

fLanguage

English

Publisher

ieee

Conference_Titel

Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on

Conference_Location

Toulouse

ISSN

1520-6149

Print_ISBN

1-4244-0469-X

Type

conf

DOI

10.1109/ICASSP.2006.1659966

Filename

1659966