Phonemic hidden Markov models with continuous mixture output densities for large vocabulary word recognition
- 1 July 1991
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Signal Processing
- Vol. 39 (7), 1677-1681
- https://doi.org/10.1109/78.134406
Abstract
No abstract availableKeywords
This publication has 12 references indexed in Scilit:
- Automatic speech recognition via pseudo-independent marginal mixturesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Mixture autoregressive hidden Markov models for speaker independent isolated word recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- On hidden Markov models in isolated word recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Phoneme modelling using continuous mixture densitiesPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2003
- Modeling acoustic transitions in speech by state-interpolation hidden Markov modelsIEEE Transactions on Signal Processing, 1992
- Tied mixture continuous parameter modeling for speech recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Large vocabulary word recognition using context-dependent allophonic hidden Markov modelsComputer Speech & Language, 1990
- Modeling microsegments of stop consonants in a hidden Markov model based word recognizerThe Journal of the Acoustical Society of America, 1990
- Fast search strategy in a large vocabulary word recognizerThe Journal of the Acoustical Society of America, 1988
- Maximum likelihood estimation for multivariate mixture observations of markov chains (Corresp.)IEEE Transactions on Information Theory, 1986