Speech recognition using hidden Markov model decomposition and a general background speech model
- 1 January 1992
- conference paper
- Published by Institute of Electrical and Electronics Engineers (IEEE)
- Vol. 1 (15206149) , 253-256 vol.1
- https://doi.org/10.1109/icassp.1992.225924
Abstract
Hidden Markov model (HMM) decomposition is used for recognizing speech in the presence of an interfering background speaker. The foreground speech is modeled by a set of left-to-right isolated word HMMs trained on a small isolated word database, and the background speech is modeled by a parallel ergodic HMM trained on a subset of TIMIT. The standard output approximation (OA) method of estimating the output probability distributions is used, and compared with a simple model combination (MC) technique. Recent work in this area has shown the effectiveness of vocabulary-specific background speech models, and hence this is used as a baseline. The results show that the general ergodic background model is as effective as a vocabulary-specific model. However, the MC technique is not effective.Keywords
This publication has 4 references indexed in Scilit:
- Hidden Markov model decomposition of speech and noisePublished by Institute of Electrical and Electronics Engineers (IEEE) ,2002
- An improved approach to the hidden Markov model decomposition of speech and noisePublished by Institute of Electrical and Electronics Engineers (IEEE) ,1992
- Simultaneous model re-estimation from contaminated data by composed hidden Markov modelingPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1991
- Speech recognition using noise-adaptive prototypesIEEE Transactions on Acoustics, Speech, and Signal Processing, 1989