A distance measure between GMMs based on the unscented transform and its application to speaker recognition

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.