Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains
- 1 April 1994
- journal article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Speech and Audio Processing
- Vol. 2 (2) , 291-298
- https://doi.org/10.1109/89.279278
Abstract
In this paper, a framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented. Three key issues of MAP estimation, namely, the choice of prior distribution family, the specification of the parameters of prior densities, and the evaluation of the MAP estimates, are addressed. Using HMM's with Gaussian mixture state observation densities as an example, it is assumed that the prior densities for the HMM parameters can be adequately represented as a product of Dirichlet and normal-Wishart densities. The classical maximum likelihood estimation algorithms, namely, the forward-backward algorithm and the segmental k-means algorithm, are expanded, and MAP estimation formulas are developed. Prior density estimation issues are discussed for two classes of applications/spl minus/parameter smoothing and model adaptation/spl minus/and some experimental results are given illustrating the practical interest of this approach. Because of its adaptive nature, Bayesian learning is shown to serve as a unified approach for a wide range of speech recognition applications.< >Keywords
This publication has 23 references indexed in Scilit:
- A learning procedure for speaker-dependent word recognition systems based on sequential processing of input tokensPublished by Institute of Electrical and Electronics Engineers (IEEE) ,2005
- Cross-lingual experiments with phone recognitionPublished by Institute of Electrical and Electronics Engineers (IEEE) ,1993
- Bayesian learning for hidden Markov model with Gaussian mixture state observation densitiesSpeech Communication, 1992
- MAP estimation of continuous density HMMPublished by Association for Computational Linguistics (ACL) ,1992
- Vocabulary and Environment Adaptation in Vocabulary-Independent Speech RecognitionPublished by Defense Technical Information Center (DTIC) ,1992
- Bayesian learning of Gaussian mixture densities for hidden Markov modelsPublished by Association for Computational Linguistics (ACL) ,1991
- The segmental K-means algorithm for estimating parameters of hidden Markov modelsIEEE Transactions on Acoustics, Speech, and Signal Processing, 1990
- Dynamic speaker adaptation for feature-based isolated word recognitionIEEE Transactions on Acoustics, Speech, and Signal Processing, 1987
- Maximum-Likelihood Estimation for Mixture Multivariate Stochastic Observations of Markov ChainsAT&T Technical Journal, 1985
- On distributions admitting a sufficient statisticTransactions of the American Mathematical Society, 1936