Average-Voice-Based Speech Synthesis Using HSMM-Based Speaker Adaptation and Adaptive Training(Speech and Hearing)
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概要
- 論文の詳細を見る
In speaker adaptation for speech synthesis, it is desirable to convert both voice characteristics and prosodic features such as F0 and phone duration. For simultaneous adaptation of spectrum, F0 and phone duration within the HMM framework, we need to transform not only the state output distributions corresponding to spectrum and F0 but also the duration distributions corresponding to phone duration. However, it is not straightforward to adapt the state duration because the original HMM does not have explicit duration distributions. Therefore, we utilize the framework of the hidden semi-Markov model (HSMM), which is an HMM having explicit state duration distributions, and we apply an HSMM-based model adaptation algorithm to simultaneously transform both the state output and state duration distributions. Furthermore, we propose an HSMM-based adaptive training algorithm to simultaneously normalize the state output and state duration distributions of the average voice model. We incorporate these techniques into our HSMM-based speech synthesis system, and show their effectiveness from the results of subjective and objective evaluation tests.
- 社団法人電子情報通信学会の論文
- 2007-02-01
著者
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Yamagishi Junichi
Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology
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Kobayashi Takao
Interdisciplinary Graduate School Of Science And Engineering Tokyo Institute Of Technology
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