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In the continuous density hidden Markov model for speech recognition, the number of mixture components in each state is usually fixed throughout all the states. The authors propose the use of a different number of mixture components for each state. For this purpose, a method is also proposed for determining the number of mixture components from the entropy information of each state. The recognition...
A technique for smoothing hidden Markov model parameters based on the concepts of deleted estimation and probabilistic mapping is proposed. The proposed algorithm is closely related to deleted interpolation in its approach and is shown to yield higher recognition rate than the distance-based smoothing and co-occurrence smoothing methods.<<ETX>>
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