Speaker identification and verification using support vector machines and sparse kernel logistic regression

Authors:
Marcel Katz;Sven E. Krüger;Martin Schafföner;Edin Andelic;Andreas Wendemuth
Affiliations:
IESK, Cognitive Systems, University of Magdeburg, Germany;IESK, Cognitive Systems, University of Magdeburg, Germany;IESK, Cognitive Systems, University of Magdeburg, Germany;IESK, Cognitive Systems, University of Magdeburg, Germany;IESK, Cognitive Systems, University of Magdeburg, Germany
Venue:
IWICPAS'06 Proceedings of the 2006 Advances in Machine Vision, Image Processing, and Pattern Analysis international conference on Intelligent Computing in Pattern Analysis/Synthesis
Year:
2006

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Abstract

In this paper we investigate two discriminative classification approaches for frame-based speaker identification and verification, namely Support Vector Machine (SVM) and Sparse Kernel Logistic Regression (SKLR). SVMs have already shown good results in regression and classification in several fields of pattern recognition as well as in continuous speech recognition. While the non-probabilistic output of the SVM has to be translated into conditional probabilities, the SKLR produces the probabilities directly. In speaker identification and verification experiments both discriminative classification methods outperform the standard Gaussian Mixture Model (GMM) system on the POLYCOST database.