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Title: Evaluation on Unsupervised Speaker Adaptation Based on Sufficient HMM Statictics of Selected Speakers
Authors: Shinichi Yoshizawa
Akira Baba
Kanako Matsunami
Yuichiro Mera
Miichi Yamada
Akinobu Lee
Kiyohiro Shikano
Issue Date: Sep-2001
Start page: 1219
End page: 1222
Abstract: This paper describes an efficient method of unsupervised speaker adaptation. This method is based on (1) selecting a subset of speakers who are acoustically close to a test speaker, and (2) calculating adapted model parameters according to the previously stored sufficient statistics of the selected speakers' data. In this method, only a few unsupervised test speaker's data are necessary for the adaptation. Also, by using the sufficient HMM statistics of the selected speakers' data, a quick adaptation can be done. Compared with a pre-clustering method, the proposed method can obtain a more optimal cluster because the clustering result is determined according to test speaker's data on-line. Experimental results show that the proposed method attains better improvement than MLLR from the speaker-independent model. The proposed method is evaluated in details and discussed.
Description: EUROSPEECH2001: the 7th European Conference on Speech Communication and Technology, September 3-7, 2001, Aalborg, Denmark.
ISSN: 1018-4074
Rights: Copyright 2001 ISCA
Text Version: Publisher
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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