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Cited by 2,108

The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.

Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains

Gauvain, J.-L., Chin-Hui Lee·1994·IEEE Transactions on Speech and Audio Processing
DOI 10.1109/89.279278
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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.>

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Research works printed but not linked: 9

  1. 10.1109/ICASSP.1983.1171906 · doi:10.1109/icassp.1983.1171906
  2. 10.3115/112405.112457 · doi:10.3115/112405.112457
  3. hon, 1992, Vocabulary-independent speech recognition The VOCIND system, 10.21236/ADA457730 · doi:10.21236/ada457730
  4. johnson, 1972, Statistical Distributions
  5. darmois, 1935, Sur les lois de probabilit� � estimation exhaustive, C R Acad Sci, 260, 1265
  6. 10.1109/ICASSP.1983.1172084 · doi:10.1109/icassp.1983.1172084
  7. ferretti, 1989, Large-vocabulary speech recognition with speaker-adapted codebook and HMM parameters, Proc Eurospeech-89, 154
  8. price, 1988, A database for continuous speech recognition in a 1000-word domain, Proc ICASSP-88, 651
  9. 10.1007/978-3-642-87934-0 · doi:10.1007/978-3-642-87934-0

Cited by

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Metadata

Authors
Gauvain, J.-L., & Lee, C.-H.
Venue
IEEE Transactions on Speech and Audio Processing
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Published
1994-04
Volume
2
Issue
2
Pages
291-298
ISSN
1063-6676 (ISSN-L)
DOI
10.1109/89.279278
OpenAlex
W2100969003
Cited by
2,108
References
26
FWCI
14.77
Type
journal article
Language
en
Open access
closed
Licence
ieeexplore.ieee.org/Xplorehelp/downloads/license-information/IEEE.html (vor)

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CoChat. (2026). Citation check: Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. https://app.cochat.ai/papers/10.1109/89.279278
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Gauvain, J.-L., & Lee, C.-H. (1994). Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains. IEEE Transactions on Speech and Audio Processing. https://doi.org/10.1109/89.279278

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