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Support vector machines and Joint Factor Analysis for speaker verification

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8 Author(s)
Dehak, N. ; Centre de Rech. Inf. de Montreal (CRIM), Montreal, QC ; Kenny, P. ; Dehak, R. ; Glembek, O.
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This article presents several techniques to combine between support vector machines (SVM) and joint factor analysis (JFA) model for speaker verification. In this combination, the SVMs are applied to different sources of information produced by the JFA. These informations are the Gaussian mixture model supervectors and speakers and common factors. We found that using SVM in JFA factors gave the best results especially when within class covariance normalization method is applied in order to compensate for the channel effect. The new combination results are comparable to other classical JFA scoring techniques.

Published in:

Acoustics, Speech and Signal Processing, 2009. ICASSP 2009. IEEE International Conference on

Date of Conference:

19-24 April 2009