A hidden Markov model with optimized inter-frame dependence

An optimized hidden Markov model (HMM) with two kinds of inter-frame dependent observation structures, both built on the observation densities of a first-order dependent form, is presented to account for the statistical dependence between successive frames. In the first model, the dependence relation among the frames is determined optimally by maximizing the likelihood of the observations in both training and testing. In the second model, the dependence structure associated with each frame is described by a weighted sum of the conditional densities of the frame given individual previous frames. The segmental K-means and the forward-backward algorithms are implemented, respectively, for the estimation of the parameters of the two models. Experimental comparisons for an isolated word recognition task show that these models achieve better performance than both the standard continuous HMM and the bigram-constrained HMM