Bayesian learning of the SCHMM parameters for speech recognition

A theoretical framework for Bayesian adaptive learning of semi-continuous HMM parameters is presented. Formulations of MAP estimation of SCHMM parameters are developed. An empirical Bayes method to estimate the hyperparameters of prior densities based on the moment estimate is proposed. Practical issues related to the use of the proposed technique for speaker adaptation application are studied. Effects of various adaptation schemes are examined and their viability is confirmed in a series of comparative experiments using a 26-word English alphabet vocabulary. The proposed method is applicable to other problems in HMM training for speech recognition such as sequential training, context adaptation and parameter smoothing.<
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