Isolated word recognition using interframe dependent hidden Markov models

A new hidden Markov model (HMM) with first-order dependent observation densities is presented to account for the statistical dependence between successive frames. In this model, the dependence relation among the frames is optimized to maximize the likelihood for both the training and testing data. Experimental comparisons with the standard continuous density HMM as well as the conditional-observation HMM for an isolated word recognition task show that a significant performance improvement is achieved for the new model.<>