Title :
Incorporation of Pentaphone-Context Dependency Based on Hybrid Hmm/Bn Acoustic Modeling Framework
Author :
Sakti, Sakriani ; Markov, Konstantin ; Nakamura, Satoshi
Author_Institution :
ATR Spoken Language Commun. Res. Lab., Kyoto
Abstract :
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
Keywords :
Bayes methods; acoustics; decoding; hidden Markov models; speech coding; speech recognition; Bayesian network; HMM; LVCSR; acoustic modeling framework; large-vocabulary continuous word recognition tasks; pentaphone-context dependency; phoneme recognition; standard decoding system; triphone framework; triphone state output distributions; Automatic speech recognition; Cities and towns; Computational efficiency; Computational modeling; Context modeling; Decoding; Hidden Markov models; Natural languages; Probability distribution; Speech recognition;
Conference_Titel :
Acoustics, Speech and Signal Processing, 2006. ICASSP 2006 Proceedings. 2006 IEEE International Conference on
Conference_Location :
Toulouse
Print_ISBN :
1-4244-0469-X
DOI :
10.1109/ICASSP.2006.1660236