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We propose a dynamic time-warping (DTW) based distortion measure for measuring the dissimilarity between pairs of left-to-right continuous density hidden Markov models with state observation densities being mixture of Gaussians. The local distortion score required in DTW is defined as an approximate Kullback-Leibler divergence (KLD) between two Gaussian mixture models (GMMs). Several approximate KLDs are studied and compared for pairs of GMMs with different properties, and one of them is identified for being used in our DTWbased HMM dissimilarity measure. In an experiment of identifying automatically the subsets of confusable Putonghua (Mandarin Chinese) syllables, it is observed that the result based on the proposed HMM dissimilarity measure is highly consistent with the one based on syllable recognition confusion matrix obtained on a testing data set.