Interspeech'2005 - Eurospeech

Lisbon, Portugal
September 4-8, 2005

A Distance Measure Between GMMs Based on the Unscented Transform and its Application to Speaker Recognition

Jacob Goldberger, Hagai Aronowitz

Bar-Ilan University, Israel

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.

Full Paper

Bibliographic reference.  Goldberger, Jacob / Aronowitz, Hagai (2005): "A distance measure between GMMs based on the unscented transform and its application to speaker recognition", In INTERSPEECH-2005, 1985-1988.