This paper presents a new method of modeling pentaphone-context units using the hybrid HMM/BN acoustic modeling. Rather than modeling pentaphones explicitly, in this approach we extend the modeled phonetic context within the triphone framework, since the probabilistic dependencies between the triphone context unit and the second preceding/following contexts are incorporated into the triphone state output distributions by means of the BN. Another advantage is that we can use a standard decoding system by assuming the next preceding/following context variables hidden during recognition. In this study, the performance of pentaphone HMM/BN model was evaluated with our LVCSR system by phoneme recognition and by large-vocabulary continuous word recognition tasks. In both cases, we observed consistently improved performance over the standard HMM based triphone model with the same number of parameters.
['Sakriani Sakti', 'Konstantin Markov', 'Satoshi Nakamura']
Incorporation of Pentaphone-Context Dependency Based on Hybrid Hmm/Bn Acoustic Modeling Framework