• Publications
  • Influence
Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains
  • J. Gauvain, C. Lee
  • Mathematics, Computer Science
  • IEEE Trans. Speech Audio Process.
  • 1 April 1994
TLDR
A framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented. Expand
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  • 202
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Minimum classification error rate methods for speech recognition
TLDR
A critical component in the pattern matching approach to speech recognition is the training algorithm, which aims at producing typical (reference) patterns or models for accurate pattern comparison. Expand
  • 715
  • 68
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An Experimental Study on Speech Enhancement Based on Deep Neural Networks
TLDR
This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with multiple-layer deep architecture. Expand
  • 581
  • 37
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A maximum-likelihood approach to stochastic matching for robust speech recognition
TLDR
We propose a maximum-likelihood (ML) stochastic matching approach to decrease the acoustic mismatch between a test utterance and a given set of speech models so as to reduce the recognition performance degradation caused by distortions in the test utterances and/or the model set. Expand
  • 414
  • 27
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A study on speaker adaptation of the parameters of continuous density hidden Markov models
TLDR
For a speech-recognition system based on continuous-density hidden Markov models (CDHMM), speaker adaptation of the parameters of CDHMM is formulated as a Bayesian learning procedure. Expand
  • 319
  • 21
Structural maximum a posteriori linear regression for fast HMM adaptation
TLDR
We propose MAPLR, a maximum a posteriori(MAP) based version of the well-known maximum likelihood linear regression (MLLR) algorithm. Expand
  • 140
  • 15
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Automatic Speech and Speaker Recognition: Advanced Topics
TLDR
Automatic Speech and Speaker Recognition: Advanced Topics groups together in a single volume a number of important topics on speech and speaker recognition, topics which are of fundamental importance, but not yet covered in detail in existing textbooks. Expand
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A Vector Space Modeling Approach to Spoken Language Identification
TLDR
We propose a novel approach to automatic spoken language identification (LID) based on vector space modeling (VSM). Expand
  • 241
  • 13
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Maximum a posteriori linear regression for hidden Markov model adaptation
TLDR
A Bayesian counterpart of the well known maximum likelihood linear regression (MLLR) adaption is formulated based on maximum a posteriori estimation. Expand
  • 147
  • 13
  • PDF
A structural Bayes approach to speaker adaptation
TLDR
This paper describes a structural maximum a posteriori (SMAP) approach to improve the MAP estimates obtained when the amount of adaptation data is small. Expand
  • 178
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