Trajectory training considering global variance for speech synthesis based on neural networks

ICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings

Volumn 2016-May, Issue , 2016, Pages 5600-5604

  Hashimoto, Kei ^a   Oura, Keiichiro ^a   Nankaku, Yoshihiko ^a   Tokuda, Keiichi ^a  

^a NAGOYA INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

global variance; neural network; Speech synthesis; statistical model; trajectory model

Indexed keywords

EID: 84973375140     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2016.7472749     Document Type: Conference Paper

References (21)

1. H. Zen, K. Tokuda, and A. W. Black, "Statistical parametric speech synthesis, " Speech Communication, vol. 51, no. 11, pp. 1039-1064, 2009
2. K. Tokuda, Y. Nankaku, T. Toda, H. Zen, J. Yamagishi, and K. Oura, "Speech synthesis based on hidden Markov models, " Proceedings of the IEEE, vol. 101, no. 5, pp. 1234-1252, 2013
3. T. Yoshimura, K. Tokuda, T. Masuko, T. Kobayashi, and T. Ki-tamura, "Simultaneous modeling of spectrum, pitch and du-ration in HMM-based speech synthesis, " Proceedings of Eu-rospeech 1999, pp. 2347-2350, 1999
4. S. Young, J. J. Odell, and P. Woodland, "Tree-based state ty-ing for high accuracy acoustic modelling, " Proceedings of ARPA Workshop on Human Language Technology, pp. 307-312, 1994
5. K. Tokuda, T. Yoshimura, T. Masuko, T. Kobayashi, and T. Ki-tamura, "Speech parameter generation algorithms for HMM-based speech synthesis, " Proceedings of ICASSP 2000, pp. 936-939, 2000
6. G. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Sainath, and B. Kings-bury, "Deep neural networks for acoustic modeling in speech recognition, " IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012
7. H. Zen, A. Senior, and M. Schuster, "Statistical parametric speech synthesis using deep neural networks, " Proceedings of ICASSP 2013, pp. 7962-7966, 2013
8. H. Lu, S. King, and O. Watts, "Combining a vector space representation of linguistic context with a deep neural network for text-to-speech synthesis, " Proceedings of ISCA SSW8, pp. 281-285, 2013
9. Y. Qian, Y. Fan, H. Wenping, and F. K. Soong, "On the training aspects of deep neural network (DNN) for parametric TTS syn-thesis, " Proceedings of ICASSP 2014, pp. 3857-3861, 2014
10. T. Toda and K. Tokuda, "A speech parameter generation algo-rithm considering global variance for HMM-based speech syn-thesis, " IEICE Transactions on Information & Systems, vol. E90-D, no. 5, pp. 816-824, 2007
11. M. Shannon andW. Byrne, "Fast, low-artifact speech synthesis considering global variance, " Proceedings of ICASSP 2013, pp. 7869-7873, 2013
12. T. Toda and S. Young, "Trajectory training considering global variance for HMM-based speech synthesis, " Proceedings of ICASSP 2009, pp. 4025-4028, 2009
13. K. Hashimoto, K. Oura, Y. Nankaku, and K. Tokuda, "The effect of neural networks in statistical parametric speech syn-thesis, " Proceedings of ICASSP 2015, pp. 4455-4459, 2015
14. H. Zen, K. Tokuda, and T. Kitamura, "Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic features, " Computer Speech and Language, vol. 21, no. 1, pp. 153-173, 2007
15. Z. Wu and S. King, "Minimum trajectory error training for deep neural networks, combined with stacked bottleneck fea-tures, " Proceedings of Interspeech 2015, pp. 309-313, 2015
16. Y. Fan, Y. Qian, F. K. Soong, and L. He, "Sequence generation error (SGE) minimization based deep neural networks training for text-to-speech synthesis, " Proceedings of Interspeech 2015, pp. 864-868, 2015
17. F. L. Xie, Y. Qian, Y. Fan, F. K. Soong, and H. Li, "Sequence er-ror SE minimization training of neural network for voice con-version, " Proceedings of Interspeech 2014, pp. 2283-2287, 2014
18. A. Kurematsu, K. Takeda, Y. Sagisaka, S. Katagiri, H. Kuwabara, and K. Shikano, "ATR Japanese speech database as a tool of speech recognition and synthesis, " Speech Commu-nication, vol. 9, pp. 357-363, 1990
19. H. Kawahara, I. Masuda-Katsuse, and A. Cheveigne, "Re-structuring speech representations using a pitch-adaptive time-frequency smoothing and an instantaneous-frequency-based F0 extraction: Possible role of a repetitive structure in sounds, " Speech Communication, vol. 27, pp. 187-207, 1999
20. K. Shinoda and T. Watanabe, "Acoustic modeling based on the MDL criterion for speech recognition, " Proceedings of Eu-rospeech 1997, pp. 99-102, 1997
21. H. Zen and A. Senior, "Deep mixture density networks for acoustic modeling in statistical parametric speech synthesis, " Proceedings of ICASSP 2014, pp. 3872-3876, 2014.