Abstract
We present a novel approach to hidden Markov model (HMM) state clustering based on the use of broad phone classes and an allophone class entropy measure. Most state-of-the-art large-vocabulary speech recognizers are based on context-dependent (CD) phone HMMs that use Gaussian mixture models for the state-conditioned observation densities. A common approach for robust HMM parameter estimation is to cluster HMM states where each state cluster shares a set of parameters such as the components of a Gaussian mixture model. Our algorithm allows clustering across allophone class boundaries by defining broad phone groups within which two states from different allophone classes can be clustered together. An allophone class entropy measure is used to control the clustering of states belonging to different allophone classes. Experimental results on three test sets are presented.
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