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SPEECH
1998

Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition

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Heteroscedastic discriminant analysis and reduced rank HMMs for improved speech recognition
We present the theory for heteroscedastic discriminant analysis (HDA), a model-based generalization of linear discriminant analysis (LDA) derived in the maximum-likelihood framework to handle heteroscedastic-unequal variance-classi®er models. We show how to estimate the heteroscedastic Gaussian model parameters jointly with the dimensionality reducing transform, using the EM algorithm. In doing so, we alleviate the need for an a priori ad hoc class assignment. We apply the theoretical results to the problem of speech recognition and observe word-error reduction in systems that employed both diagonal and full covariance heteroscedastic Gaussian models tested on the TI-DIGITS database. Ó 1998 Elsevier Science B.V. All rights reserved.
Nagendra Kumar, Andreas G. Andreou
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 1998
Where SPEECH
Authors Nagendra Kumar, Andreas G. Andreou
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