A maximum a posteriori approach to speaker adaptation using the trended hidden Markov model

IEEE Transactions on Speech and Audio Processing

Volumn 9, Issue 5, 2001, Pages 549-557

  Chengalvarayan, Rathinavelu ^a,b   Deng, Li ^a,c  

^a IEEE   (United States)

^b NOKIA RESEARCH CENTER   (United States)

^c MICROSOFT RESEARCH   (United States)

Author keywords

Adaptation; Hidden Markov models; MAP estimation; Polynomial functions; Speech recognition

Indexed keywords

HIDDEN MARKOV MODEL; SPEAKER ADAPTATION;

MARKOV PROCESSES; MATHEMATICAL MODELS; PARAMETER ESTIMATION; POLYNOMIALS;

SPEECH RECOGNITION;

EID: 0035396159     PISSN: 10636676     EISSN: None     Source Type: Journal    
DOI: 10.1109/89.928919     Document Type: Article

References (18)

1. Use of generalized dynamic feature parameters for speech recognition
2. Speech trajectory discrimination using the minimum classification error learning
3. Speech adaptation experiments using nonstationary-state HMMs: A MAP approach
4. Comparison of parametric representations for monosyllabic word recognition in continuously spoken sentences
5. Maximum likelihood from incomplete data via the EM algorithm
6. A generalized HMM with state-conditioned trend functions of time for the speech signal
7. Speech recognition using hidden Markov models with polynomial regression functions as nonstationary states
8. Speaker-independent phonetic classification using hidden Markov models with state-conditioned mixtures of trend functions
9. Bayesian learning for hidden Markov model with Gaussian mixture state observation densities
10. Maximum a posteriori estimation for multivariate Gaussian mixture observation of Markov chains
11. Bayesian adaptive learning of the parameters of hidden Markov model for speech recognition
12. Speech recognition using a linear dynamic segmental HMM
13. Adaptation of spectral trajectory models for large vocabulary continuous speech recognition
14. Adaptation of polynomial trajectory segment models for large vocabulary speech recognition
15. A study on speaker adaptation of the parameters of continuous density hidden Markov models
16. Bayesian adaptive learning and MAP estimation of HMM
17. From HMM's to segment models: A unified view of stochastic modeling for speech recognition