Unsupervised Speaker Adaptation Based on Sufficient HMM Statistics of Selected Speakers
| Autor: | Yuichiro Mera, K. Matsunami, Shinichi Yoshizawa, Akira Baba, M. Yamada, Kiyohiro Shikano |
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| Jazyk: | angličtina |
| Rok vydání: | 2001 |
| Předmět: | |
| Zdroj: | ICASSP |
| Popis: | 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 ICASSP2001: IEEE International Conference on Acoustics, Speech and Signal Processing, May 7-11, 2001, Salt Lake City, Utah, US. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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