HMM state clustering based on efficient cross-validation

Decision tree state clustering is explored using a cross validation likelihood criterion. Cross-validation likelihood is more reliable than conventional likelihood and can be efficiently computed using sufficient statistics. It results in a better tying structure and provides a termination criterion that does not rely on empirical thresholds. Large vocabulary recognition experiments on conversational telephone speech show that, for large numbers of tied states, the cross-validation method gives more robust results.