Publication Details
Support vector machines and joint factor analysis for speaker verification
Kenny Patrick
Dehak Reda
Glembek Ondřej, Ing., Ph.D.
Dumouchel Pierre
Burget Lukáš, doc. Ing., Ph.D. (DCGM)
Hubeika Valiantsina, Ing.
Castaldo Fabio
Joint Factor Analysis, Support Vector Machine, Speaker factors space, Within Class Covariance Normalization
The paper is on support vector machines and joint factor analysis. Several variants of joint factor analysis are tested.
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 in different sources of information produced by the JFA. These informations are the Gaussian Mixture Model supervectors and speakers and Common factors. We found that the use of JFA factors gave the best results especially when within class covariance normalization method is applied in the speaker factors space in order to compensate for the channel effect. The new combination results are comparable to other classical JFA scoring techniques.
@inproceedings{BUT33739,
author="Najim {Dehak} and Patrick {Kenny} and Reda {Dehak} and Ondřej {Glembek} and Pierre {Dumouchel} and Lukáš {Burget} and Valiantsina {Hubeika} and Fabio {Castaldo}",
title="Support vector machines and joint factor analysis for speaker verification",
booktitle="Proc. ICASSP 2009",
year="2009",
pages="1--4",
publisher="IEEE Signal Processing Society",
address="Taiwan",
isbn="978-1-4244-2354-5",
url="http://www.fit.vutbr.cz/research/groups/speech/publi/2009/NAJIM_ICASSP2009.pdf"
}