Rapid likelihood calculation of subspace clustered Gaussian components

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

Volumn 3, Issue , 2000, Pages 1519-1522

  Aiyer, A ^a,b   Gales, M J F ^a,c   Picheny, M A ^a  

^a IBM T J WATSON RESEARCH CENTER   (United States)

^b Stanford University   (United States)

^c UNIVERSITY OF CAMBRIDGE   (United Kingdom)

Author keywords

[No Author keywords available]

Indexed keywords

COST REDUCTION; SIGNAL PROCESSING; SPEECH PROCESSING; SPEECH RECOGNITION; ACOUSTIC NOISE; ALGORITHMS; COMPUTATIONAL COMPLEXITY; MARKOV PROCESSES; MAXIMUM LIKELIHOOD ESTIMATION;

ACOUSTIC MODEL; COMPUTATIONAL COSTS; DISTRIBUTED CLUSTERING; GAUSSIAN COMPONENTS; GAUSSIAN SELECTION; PARTIAL SUMS; SPEECH RECOGNITION SYSTEMS;

GAUSSIAN DISTRIBUTION; SPEECH RECOGNITION;

GAUSSIAN COMPONENTS; HIDDEN MARKOV MODEL; META ATOM ELEMENT; SUBSPACE DISTRIBUTED CLUSTERING HIDDEN MARKOV MODEL;

EID: 0033719627     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2000.861939     Document Type: Conference Paper

References (5)

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3. B. Mak. Towards a Compact Speech Recogniser: Subspace Distribution Clustering Hidden Markov Model. PhD thesis, Oregon Graduate Institute of Science and Technology, 1998.
4. B. Mak and E. Bocchieri. Subspace distribution clustering for continuous observation density hidden Markov models. In Proceedings Eurospeech, pages 107-110, 1997.
5. S. J. Young and P. C. Woodland. The use of state tying in continuous speech recognition. In Proceedings Eurospeech, pages 2207-2210, 1993.