论文信息 - The Effect of Using Normalized Models in Statistical Speech Synthesis - 字舞流文

The Effect of Using Normalized Models in Statistical Speech Synthesis

This work was partly supported by the European Community's Seventh Framework Programme (FP7/2007-2013) under grant agreement 213845 (EMIME).

Heiga Zen | William J. Byrne | Matt Shannon | H. Zen | W. Byrne | Matt Shannon

[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] 全炳河,et al. Reformulating HMM as a trajectory model by imposing explicit relationships between static and dynamic features , 2006 .

[3] Matt Shannon,et al. A formulation of the autoregressive HMM for speech synthesis , 2009 .

[4] Keiichi Tokuda,et al. Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis , 1999, EUROSPEECH.

[5] William J. Byrne,et al. Autoregressive HMMs for speech synthesis , 2009, INTERSPEECH.

[6] Heiga Zen,et al. Hidden Semi-Markov Model Based Speech Synthesis System , 2006 .

[7] Heiga Zen,et al. Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences , 2007, Comput. Speech Lang..

[8] Heiga Zen,et al. Reformulating the HMM as a Trajectory Model , 2004 .

[9] Heiga Zen,et al. Statistical Parametric Speech Synthesis , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.