An approach to robust unsupervised speaker adaptation

In this letter, we propose an approach to robust unsupervised speaker adaptation. Usually, recognition errors made on the adaptation utterances mislead parameter estimation when a speaker adaptation algorithm is operated in an unsupervised mode. In order to alleviate this problem, we first adapt a G...

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Bibliographic Details
Published in:IEEE signal processing letters 2005-06, Vol.12 (6), p.469-472
Main Authors: Kim, Nam Soo, Seo, Dong Jin, Lim, Woohyung
Format: Article
Language:English
Subjects:
Covariance matrix
Data mining
Data processing
Degradation
Gaussian mixture model (GMM)
Hidden Markov models
Mathematical analysis
Parameter estimation
Robustness
Smoothing methods
Speech recognition
unsupervised speaker adaptation
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Summary:In this letter, we propose an approach to robust unsupervised speaker adaptation. Usually, recognition errors made on the adaptation utterances mislead parameter estimation when a speaker adaptation algorithm is operated in an unsupervised mode. In order to alleviate this problem, we first adapt a Gaussian mixture model (GMM) and then transform the hidden Markov model (HMM) parameters according to the information extracted from GMM adaptation.
ISSN:1070-9908
1558-2361
DOI:10.1109/LSP.2005.847863