A Bayesian approach to HMM-based speech synthesis
暂无分享,去创建一个
[1] 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).
[2] Jj Odell,et al. The Use of Context in Large Vocabulary Speech Recognition , 1995 .
[3] K. Tokuda,et al. Speech parameter generation from HMM using dynamic features , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.
[4] Hagai Attias,et al. Inferring Parameters and Structure of Latent Variable Models by Variational Bayes , 1999, UAI.
[5] Keiichi Tokuda,et al. An algorithm for speech parameter generation from continuous mixture HMMs with dynamic features , 1995, EUROSPEECH.
[6] K. Koishida,et al. Vector quantization of speech spectral parameters using statistics of dynamic features , 1997 .
[7] S. J. Young,et al. Tree-based state tying for high accuracy acoustic modelling , 1994 .
[8] Shigeru Katagiri,et al. ATR Japanese speech database as a tool of speech recognition and synthesis , 1990, Speech Commun..
[9] Naonori Ueda,et al. Variational bayesian estimation and clustering for speech recognition , 2004, IEEE Transactions on Speech and Audio Processing.
[10] Keiichi Tokuda,et al. Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis , 1999, EUROSPEECH.
[11] Naonori Ueda,et al. Application of Variational Bayesian Approach to Speech Recognition , 2002, NIPS.
[12] Keiichi Tokuda,et al. Hidden Markov models based on multi-space probability distribution for pitch pattern modeling , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).
[13] Heiga Zen,et al. Bayesian context clustering using cross valid prior distribution for HMM-based speech recognition , 2008, INTERSPEECH.