Learning Prosodic Patterns for Mandarin Speech Synthesis

[1]  Stuart J. Russell,et al.  Local Learning in Probabilistic Networks with Hidden Variables , 1995, IJCAI.

[2]  Mari Ostendorf,et al.  A dynamical system model for generating fundamental frequency for speech synthesis , 1999, IEEE Trans. Speech Audio Process..

[3]  L. Santos,et al.  The Rough Sets theory , 2006 .

[4]  Yung-Hwan Oh,et al.  Tree-based modeling of prosodic phrasing and segmental duration for Korean TTS systems , 1999, Speech Commun..

[5]  J. Suzuki An extension on learning Bayesian belief networks based on MDL principle , 1995, Proceedings of 1995 IEEE International Symposium on Information Theory.

[6]  Alberto Maria Segre,et al.  Programs for Machine Learning , 1994 .

[7]  J. Ross Quinlan,et al.  C4.5: Programs for Machine Learning , 1992 .

[8]  Hu Qiwei Prosody learning and simulation for Chinese text to speech system , 1998 .

[9]  Sin-Horng Chen,et al.  An RNN-based prosodic information synthesizer for Mandarin text-to-speech , 1998, IEEE Trans. Speech Audio Process..

[10]  Jie Cheng,et al.  An Algorithm for Bayesian Belief Network Construction from Data , 2004 .

[11]  Joe Suzuki,et al.  Learning Bayesian Belief Networks Based on the MDL Principle : An Efficient Algorithm Using the Branch and Bound Technique , 1999 .

[12]  B. Ripley,et al.  Pattern Recognition , 1968, Nature.

[13]  Zdzislaw Pawlak,et al.  Rough classification , 1984, Int. J. Hum. Comput. Stud..

[14]  Chiu-yu Tseng,et al.  The synthesis rules in a Chinese text-to-speech system , 1989, IEEE Trans. Acoust. Speech Signal Process..

[15]  Biing-Hwang Juang,et al.  Fundamentals of speech recognition , 1993, Prentice Hall signal processing series.

[16]  Jau-Hung Chen,et al.  Template-driven generation of prosodic information for Chinese concatenative synthesis , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).