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Duration modeling with expanded HMM applied to speech recognition

Author
Bonafonte, A.; Vidal, J.; Nogueiras, A.
Type of activity
Presentation of work at congresses
Name of edition
4th International Conference on Spoken Language Processing
Date of publication
1996
Presentation's date
1996-10
Book of congress proceedings
ICSLP 1996: 4th International Conference on Spoken Language Processing: Philadelphia, PA, USA: October 3-6, 1996
First page
1097
Last page
1100
Publisher
H. TIMOTHY BRUMMELL, WILLIAM IDSARDI CITATION DELAWARE, NEW CASTLE, DELAWARE
DOI
https://doi.org/10.1109/ICSLP.1996.607797 Open in new window
Repository
http://hdl.handle.net/2117/102630 Open in new window
URL
http://ieeexplore.ieee.org/document/607797/ Open in new window
Abstract
The occupancy of the HMM states is modeled by means of a Markov chain. A linear estimator is introduced to compute the probabilities of the Markov chain. The distribution function (DF) represents accurately the observed data. Representing the DF as a Markov chain allows the use of standard HMM recognizers. The increase of complexity is negligible in training and strongly limited during recognition. Experiments performed on acoustic-phonetic decoding shows how the phone recognition rate increases...
Citation
Bonafonte, A., Vidal, J., Nogueiras, A. Duration modeling with expanded HMM applied to speech recognition. A: International Conference on Spoken Language. "Fourth international conference on spoken language, 1996, ICSLP 96: proceedings". Philadelphia, PA: H. TIMOTHY BRUMMELL, WILLIAM IDSARDI CITATION DELAWARE, NEW CASTLE, DELAWARE, 1996, p. 1097-1100.
Keywords
Decoding, Hidden Markov models, Probability, Speech recognition
Group of research
IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Center
SPCOM - Signal Processing and Communications Group
TALP - Centre for Language and Speech Technologies and Applications
VEU - Speech Processing Group

Participants

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