SVM speaker verification using session variability modelling and GMM supervectors

Authors:
M. McLaren;R. Vogt;S. Sridharan
Affiliations:
Speech and Audio Research Laboratory, Queensland University of Technology, Brisbane, Australia;Speech and Audio Research Laboratory, Queensland University of Technology, Brisbane, Australia;Speech and Audio Research Laboratory, Queensland University of Technology, Brisbane, Australia
Venue:
ICB'07 Proceedings of the 2007 international conference on Advances in Biometrics
Year:
2007

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Abstract

This paper demonstrates that modelling session variability during GMM training can improve the performance of a GMM supervector SVM speaker verification system. Recently, a method of modelling session variability in GMM-UBM systems has led to significant improvements when the training and testing conditions are subject to session effects. In this work, session variability modelling is applied during the extraction of GMM supervectors prior to SVM speaker model training and classification. Experiments performed on the NIST 2005 corpus show major improvements over the baseline GMM supervector SVM system.