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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.
AbstractThis 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. [Edit]
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