Multiple-regression hidden Markov model
- Resource Type
- Conference
- Authors
- Fujinaga, K.; Nakai, M.; Shimodaira, H.; Sagayama, S.
- Source
- 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) Acoustics, speech, and signal processing Acoustics, Speech, and Signal Processing, 2001. Proceedings. (ICASSP '01). 2001 IEEE International Conference on. 1:513-516 vol.1 2001
- Subject
- Signal Processing and Analysis
Components, Circuits, Devices and Systems
Hidden Markov models
Frequency
Degradation
Speech recognition
Cepstral analysis
Probability distribution
Error analysis
Maximum likelihood linear regression
Context modeling
Humans
- Language
- ISSN
- 1520-6149
Proposes a class of hidden Markov model (HMM) called multiple-regression HMM (MR-HMM) that utilizes auxiliary features such as fundamental frequency (F/sub 0/) and speaking styles that affect spectral parameters to better model the acoustic features of phonemes. Though such auxiliary features are considered to be the factors that degrade the performance of speech recognizers, the proposed MR-HMM adapts its model parameters, i.e. mean vectors of output probability distributions, depending on these auxiliary information to improve the recognition accuracy. Formulation for parameter reestimation of MR-HMM based on the EM algorithm is given in the paper. Experiments of speaker-dependent isolated word recognition demonstrated that MR-HMMs using F/sub 0/ based auxiliary features reduced the error rates by more than 20% compared with the conventional HMMs.