C. Lee

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

A framework for maximum a posteriori (MAP) estimation of hidden Markov models (HMM) is presented. Expand

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Minimum classification error rate methods for speech recognition

B. Juang, Wu Hou, C. Lee
Computer Science
IEEE Trans. Speech Audio Process.
1 May 1997

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

An Experimental Study on Speech Enhancement Based on Deep Neural Networks

Yong Xu, J. Du, Li-Rong Dai, C. Lee
Computer Science
IEEE Signal Processing Letters
2014

This letter presents a regression-based speech enhancement framework using deep neural networks (DNNs) with multiple-layer deep architecture. Expand

A maximum-likelihood approach to stochastic matching for robust speech recognition

Ananth Sankar, C. Lee
Mathematics, Computer Science
IEEE Trans. Speech Audio Process.
1 May 1996

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

A study on speaker adaptation of the parameters of continuous density hidden Markov models

C. Lee, Chih-Heng Lin, B. Juang
Computer Science
IEEE Trans. Signal Process.
1 April 1991

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

Structural maximum a posteriori linear regression for fast HMM adaptation

O. Siohan, T. A. Myrvoll, C. Lee
Computer Science
Comput. Speech Lang.
2002

We propose MAPLR, a maximum a posteriori(MAP) based version of the well-known maximum likelihood linear regression (MLLR) algorithm. Expand

Automatic Speech and Speaker Recognition: Advanced Topics

C. Lee, F. Soong, K. Paliwal
Computer Science
1 August 1999

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

A Vector Space Modeling Approach to Spoken Language Identification

Haizhou Li, B. Ma, C. Lee
Computer Science
IEEE Transactions on Audio, Speech, and Language…
2007

We propose a novel approach to automatic spoken language identification (LID) based on vector space modeling (VSM). Expand

Maximum a posteriori linear regression for hidden Markov model adaptation

C. Chesta, O. Siohan, C. Lee
Computer Science
EUROSPEECH
1999

A Bayesian counterpart of the well known maximum likelihood linear regression (MLLR) adaption is formulated based on maximum a posteriori estimation. Expand

A structural Bayes approach to speaker adaptation

Koichi Shinoda, C. Lee
Computer Science
IEEE Trans. Speech Audio Process.
1 March 2001

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

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