Score calibrating for speaker recognition based on support vector machines and Gaussian Mixture Models

Authors:
Marcel Katz;Martin Schafföner;Sven E. Krüger;Andreas Wendemuth
Affiliations:
University of Magdeburg, Germany;University of Magdeburg, Germany;University of Magdeburg, Germany;University of Magdeburg, Germany
Venue:
SIP '07 Proceedings of the Ninth IASTED International Conference on Signal and Image Processing
Year:
2007

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Abstract

In this paper we investigate three approaches of calibrating and fusing output scores for speaker verification. Today's speaker recognition systems often consist of several subsystems that use different generative and discriminative classifiers. If subsystems like Gaussian Mixture Models (GMMs) and Support Vector Machines (SVMs) are used to obtain a final score for decision a probabilistic calibration of single classifier scores is important. Experiments on the NIST 2006 evaluation dataset show a performance improvement compared to the single subsystems and the standard un-calibrated fusion methods.