Abstract
A method is proposed to reduce the number of Gaussian components in continuous density hidden Markov models (HMMs). As its initial model, the method employs a well-trained, large-sized HMM in which the components of each state's Gaussian mixture probability density function are clustered into a binary tree. For each state, a subset of Gaussian components is chosen from the Gaussian tree on the basis of the minimum description length (MDL) criterion. By varying the penalty coefficient for large size models in the MDL criterion, it is possible to obtain the total number of Gaussian components desired for smaller models. In our experimental evaluations, the proposed method successfully reduced the number of Gaussian components by 75%, with only 1% degradation in recognition accuracy.
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