Incorporating dynamic features into minimum generation error training for HMM-based speech synthesis
暂无分享,去创建一个
[1] Heiga Zen,et al. Statistical Parametric Speech Synthesis , 2007, IEEE International Conference on Acoustics, Speech, and Signal Processing.
[2] Mats Blomberg,et al. Effects of emphasizing transitional or stationary parts of the speech signal in a discrete utterance recognition system , 1982, ICASSP.
[3] S. Furui. On the role of spectral transition for speech perception. , 1986, The Journal of the Acoustical Society of America.
[4] Heiga Zen,et al. The HMM-based speech synthesis system (HTS) version 2.0 , 2007, SSW.
[5] Ren-Hua Wang,et al. Minimum Generation Error Training for HMM-Based Speech Synthesis , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.
[6] Keiichi Tokuda,et al. Speech parameter generation algorithms for HMM-based speech synthesis , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).
[7] Keiichi Tokuda,et al. An adaptive algorithm for mel-cepstral analysis of speech , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.
[8] Keiichi Tokuda,et al. Speech synthesis using HMMs with dynamic features , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.
[9] Keiichi Tokuda,et al. Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis , 1999, EUROSPEECH.
[10] Satoshi Imai,et al. Cepstral analysis synthesis on the mel frequency scale , 1983, ICASSP.
[11] Shun-ichi Amari,et al. A Theory of Adaptive Pattern Classifiers , 1967, IEEE Trans. Electron. Comput..