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Please use this identifier to cite or link to this item: http://hdl.handle.net/10061/8039

Title: Unsupervised Speaker Adaptation Based on Sufficient HMM Statistics of Selected Speakers
Authors: Shinichi Yoshizawa
Akira Baba
Kanako Matsunami
Yuichiro Mera
Miichi Yamada
Kiyohiro Shikano
Issue Date: May-2001
Publisher: IEEE
Start page: 341
End page: 344
Abstract: Describes an efficient method for 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 HMM statistics of the selected speakers' data. In this method, only a few unsupervised test speaker's data are required 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 speaker 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. Moreover the proposed method utilizes only one unsupervised sentence utterance, while MLLR usually utilizes more than ten supervised sentence utterances
Description: ICASSP2001: IEEE International Conference on Acoustics, Speech and Signal Processing, May 7-11, 2001, Salt Lake City, Utah, US.
URI: http://hdl.handle.net/10061/8039
ISBN: 0780370414
ISSN: 1520-6149
Rights: Copyright 2001 IEEE
Text Version: Publisher
Publisher DOI: 10.1109/ICASSP.2001.940837
Appears in Collections:情報科学研究科 / Graduate School of Information Science

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