Generative adversarial network-based postfilter for statistical parametric speech synthesis

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

Volumn , Issue , 2017, Pages 4910-4914

  Kaneko, Takuhiro ^a   Kameoka, Hirokazu ^a   Hojo, Nobukatsu ^b   Ijima, Yusuke ^b   Hiramatsu, Kaoru ^a   Kashino, Kunio ^a  

^a Nippon Telegraph and Telephone Corporation   (Japan)

^b NTT CORPORATION   (Japan)

Author keywords

deep neural network; generative adversarial network; postfilter; Statistical parametric speech synthesis

Indexed keywords

EID: 85023752230     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2017.7953090     Document Type: Conference Paper

References (30)

1. T. Yoshimura, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, "Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis," in Proc. Eurospeech, 1999, pp. 2347-2350.
2. H. Zen, A. Senior, and M. Schuster, "Statistical parametric speech synthesis using deep neural networks," in Proc. ICASSP, 2013, pp. 7962-7966.
3. A. Hunt and A. Black, "Unit selection in a concatenative speech synthesis system using a large speech database," in Proc. ICASSP, 1996, pp. 373-376.
4. M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi, "Adaptation of pitch and spectrum for HMM-based speech synthesis using MLLR," in Proc. ICASSP, 2001, pp. 805-808.
5. T. Nose, J. Yamagishi, T. Masuko, and T. Kobayashi, "A style control technique for HMM-based expressive speech synthesis," IEICE Trans. Inf. Syst., Vol. E90-D, no. 2, pp. 533-543, 2007.
6. A. Gutkin, X. Gonzalvo, S. Breuer, and P. Taylor, "Quantized HMMs for low footprint text-to-speech synthesis," in Proc. Interspeech, 2010.
7. H. Zen, K. Tokuda, and A. Black, "Statistical parametric speech synthesis," Speech Commn., Vol. 51, no. 11, pp. 1039-1064, 2009.
8. K. Koishida, K. Tokuda, T. Kobayashi, and S. Imai, "CELP coding based on mel-cepstral analysis," in Proc. ICASSP, 1995, pp. 33-36.
9. T. Yoshimura, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, "Incorporating a mixed excitation model and postfilter into HMM-based text-to-speech synthesis," Syst. Comput. Jpn., Vol. 36, no. 12, pp. 43-50, 2005.
10. Z. Ling, Y. Wu, Y. Wang, L. Qin, and R. Wang, "USTC system for Blizzard Challenge 2006 an improved HMM-based speech synthesis method," in Proc. Blizzard Challenge Workshop, 2006.
11. T. Toda and K. Tokuda, "A speech parameter generation algorithm considering global variance for HMM-based speech synthesis," IEICE Trans. Inf. Syst., Vol. E90-D, no. 5, pp. 816-824, 2007.
12. H. Silén, E. Heiander, J. Nurminen, and M. Gabbouj, "Ways to implement global variance in statistical speech synthesis," in Proc. Interspeech, 2012, pp. 1436-1439.
13. S. Takamichi, T. Toda, G. Neubig, S. Sakti, and S. Nakamura, "A postfilter to modify the modulation spectrum in HMM-based speech synthesis," in Proc. ICASSP, 2014, pp. 290-294.
14. L.-H. Chen, T. Raitio, C. Valentini-Botinhao, J. Yamagishi, and Z.-H. Ling, "DNN-based stochastic postfilter for HMM-based speech synthesis," in Proc. Interspeech, 2014, pp. 1954-1958.
15. L.-H. Chen, T. Raitio, C. Valentini-Botinhao, Z.-H. Ling, and J. Yamagishi, "A deep generative architecture for postfiltering in statistical parametric speech synthesis," IEEE Trans. Audio Speech and Lang. Process., Vol. 23, no. 11, pp. 2003-2014, 2015.
16. K. Hashimoto, K. Oura, Y. Nankaku, and K. Tokuda, "The effect of neural networks in statistical parametric speech synthesis," in Proc. ICASSP, 2015, pp. 4455-4459.
17. I. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. Courville, and Y. Bengio, "Generative adversarial nets," in Proc. NIPS, 2014, pp. 2672-2680.
18. E. Denton, S. Chintala, Szlam A., and R. Fergus, "Deep generative image models using a Laplacian pyramid of adversarial networks," in Proc. NIPS, 2015, pp. 1486-1494.
19. A. Radford, L. Metz, and S. Chintala, "Unsupervised representation learning with deep convolutional generative adversarial networks," in Proc. ICLR, 2016.
20. A. B. L. Larsen, S. K. Sønderby, and O. Winther, "Autoencoding beyond pixels using a learned similarity metric," in Proc. ICML, 2016.
21. M. Mirza and S. Osindero, "Conditional generative adversarial nets," in arXiv preprint arXiv: 1411.1784. 2014.
22. K. He, X. Zhang, S. Ren, and J. Sun, "Deep residual learning for image recognition," in Proc. CVPR, 2016.
23. J. Long, E. Shelhamer, and T. Darrell, "Fully convolutional networks for semantic segmentation," in Proc. CVPR, 2015, pp. 3431-3440.
24. H. Kawahara, I. Masuda-Katsuse, and A. de Cheveigné, "Restructuring 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 Commn., Vol. 27, no. 3, pp. 187-207, 1999.
25. V. Nair and G. E. Hinton, "Rectified linear units improve restricted boltzmann machines," in Proc. ICML, 2010, pp. 807-814.
26. A. Maas, A. Y. Hannun, and A. Y. Ng, "Rectifier nonlinearities improve neural network acoustic models," in Proc. ICML, 2013.
27. B. Xu, N. Wang, T. Chen, and M. Li, "Empirical evaluation of rectified activations in convolutional network," in arXiv preprint arXiv: 1505.0085'3. 2015.
28. D. P. Kingma and M. Welling, "Adam: A method for stochastic optimization," in Proc. ICLR, 2015.
29. L. Theis, A. Oord, and M. Bethge, "A note on the evaluation of generative models," in Proc. ICLR, 2016.
30. Y. Fan, Y. Qian, F.-L. Xie, and F. K. Soong, "TTS synthesis with bidirectional LSTM based recurrent neural networks," in Proc. Interspeech, 2014, pp. 1964-1968.