This paper introduces a method that can better maximize likelihood (ML) in state decision tree clustering under a continuous density hidden Markov model (CDHMM) framework. Under ML criterion, the conventional phonetic context rule based triphone clustering process is re-examined by checking the fitness for each triphone cluster within its tree node class clustered by its yeso answer to the phonetic context questions. If a triphone within its class better fits the other class (in a certain degree) by the ML standard, then its class-membership is re-assigned into the better-fit class. This method, applied at every level of three node during the tree building process, cna improve the overall likelihood of the tree therefore should help to improve system performanc at the end. Comparison experiment shows that the proposed method cuts word error rate (WER) by 6
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