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情報科学研究科 / Graduate School of Information Science >
Please use this identifier to cite or link to this item:
http://hdl.handle.net/10061/8039
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| 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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