ASRU 2001, IEEE Workshop on Automatic Speech Recognition and Understanding, December 9-13, 2001, Madonna di Campiglio, Trento, Italy
HMM systems exhibit a large amount of redundancy. To this end, a technique called Eigenvoices was found to be very effective for speaker adaptation. The correlation between HMM parameters is exploited via a linear constraint called eigenspace. This constraint is obtained through a PCA analysis of the training speakers. In this paper, we show how PCA can be linked to the maximum-likelihood criterion. Then, we extend the method to LDA transformations and piecewise linear constraints. On the Wall Street Journal (WSJ) dictation task, we obtain 1.7% WER improvement (15% relative) when using self-adaptation.
Type:
Conférence
City:
Trento
Date:
2001-12-09
Department:
Sécurité numérique
Eurecom Ref:
713
Copyright:
© 2001 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
See also:
