PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

A distance measure between GMMs based on the unscented transform and its application to speaker recognition
Jacob Goldberger and Hagai Aronowitz
In: INTERSPEECH 2005, 4-8 Sep 2005, Lisbon, Portugal.

Abstract

This paper proposes a dissimilarity measure between two Gaussian mixture models (GMM). Computing a distance measure between two GMMs that were learned from speech segments is a key element in speaker verification, speaker segmentation and many other related applications. A natural measure between two distributions is the Kullback-Leibler divergence. However, it cannot be analytically computed in the case of GMM. We propose an accurate and efficiently computed approximation of the KL-divergence. The method is based on the unscented transform which is usually used to obtain a better alternative to the extended Kalman filter. The suggested distance is evaluated in an experimental setup of speakers data-set. The experimental results indicate that our proposed approximations outperform previously suggested methods.

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EPrint Type:Conference or Workshop Item (Paper)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Computational, Information-Theoretic Learning with Statistics
Speech
ID Code:1562
Deposited By:Jacob Goldberger
Deposited On:28 November 2005