An HMM-based text segmentation method using variational bayes approach and its application to LVCSR for broadcast news

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn I, Issue , 2005, Pages 485-488

  Koshinaka, Takafumi ^a   Iso, Ken Ichi ^a   Okumura, Akitoshi ^a  

^a NEC CORPORATION   (Japan)

Author keywords

[No Author keywords available]

Indexed keywords

BROADCASTING; COMPUTATIONAL METHODS; INFORMATION RETRIEVAL; MATHEMATICAL MODELS; PARAMETER ESTIMATION; SPEECH RECOGNITION;

HIDDEN MARKOV MODEL (HMM); LARGE VOCABULARY CONTINUOUS SPEECH RECOGITION (LVCSR); VARIATIONAL BAYES FRAMEWORK;

TEXT PROCESSING;

EID: 33646764162     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2005.1415156     Document Type: Conference Paper

References (5)

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3. H. Attias, "Inferring parameters and structure of latent variable models by variational bayes," Proc. 15th Conf. on Uncertainty in Artificial Intelligence, 1999.
4. D. Beeferman, A. Berger, and J. Lafferty, "Statistical models for text segmentation," Machine Learning, Vol.34, No. 1-3, pp. 177-210, 1999.
5. T. Hofmann, "Probabilistic latent semantic indexing," Proc. 22ndInt'l Conf. on R&D in Information Retrieval (SIGIR'99), 1999.