Upper Bound Kullback-Leibler Divergence for Hidden Markov Models with Application as Discrimination Measure for Speech Recognition

This paper presents a criterion for defining an upper bound Kullback-Leibler divergence (UB-KLD) for Gaussian mixtures models (GMMs). An information theoretic interpretation of this indicator and an algorithm for calculating it based on similarity alignment between mixture components of the models are proposed. This bound is used to characterize an upper bound closed-form expression for the Kullback-Leibler divergence (KLD) for left-to-right transient hidden Markov models (HMMs), where experiments based on real speech data show that this indicator precisely follows the discrimination tendency of the actual KLD