Modeling spectral envelopes using restricted Boltzmann machines for statistical parametric speech synthesis

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

Volumn , Issue , 2013, Pages 7825-7829

  Ling, Zhen Hua ^a,b   Deng, Li ^c   Yu, Dong ^c  

^a National Engineering Laboratory for Speech and Language Information Processing   (China)

^b UNIVERSITY OF WASHINGTON   (United States)

^c MICROSOFT RESEARCH   (United States)

Author keywords

hidden Markov model; restricted Boltzmann machine; spectral envelope; Speech synthesis

Indexed keywords

CONVENTIONAL METHODS; GAUSSIAN MIXTURE MODEL; GENERALIZATION ABILITY; HMM-BASED SPEECH SYNTHESIS; JOINT DISTRIBUTIONS; RESTRICTED BOLTZMANN MACHINE; SPECTRAL ENVELOPES; STATISTICAL PARAMETRIC SPEECH SYNTHESIS;

HIDDEN MARKOV MODELS; SIGNAL PROCESSING;

SPEECH SYNTHESIS;

EID: 84890447002     PISSN: 15206149     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1109/ICASSP.2013.6639187     Document Type: Conference Paper

References (15)

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 Eurospeech, 1999, pp. 2347-2350.
2. K. Tokuda, T. Yoshimura, T. Masuko, T. Kobayashi, and T. Kitamura, "Speech parameter generation algorithms for HMMbased speech synthesis," in ICASSP, 2000, vol. 3, pp. 1315-1318.
3. H. Kawahara, I. Masuda-Katsuse, and A. de Cheveigne, "Restructuring speech representations using pitch-adaptive timefrequency smoothing and an instantaneous-frequency-based F0 extraction: possible role of a repetitive structure in sounds," Speech Communication, vol. 27, pp. 187-207, 1999.
4. H. Zen, T. Toda, M. Nakamura, and K. Tokuda, "Details of Nitech HMM-based speech synthesis system for the Blizzard Challenge 2005," IEICE Trans. Inf. &Syst., vol. E90-D, no. 1, pp. 325-333, 2007.
5. Z.-H. Ling, Y.-J. Wu, Y.-P. Wang, L. Qin, and R.-H. Wang, "USTC system for Blizzard Challenge 2006: an improved HMM-based speech synthesis method," in Blizzard Challenge Workshop, 2006.
6. P. Smolensky, "Information processing in dynamical systems: Foundations of harmony theory," in Parallel Distributed Processing, D.E. Rumelhart and McClelland J.L., Eds., vol. 1, chapter 6, pp. 194-281. MIT Press, 1986.
7. R. Salakhutdinov, Learning deep generative models, Ph.D. thesis, University of Toronto, 2009.
8. G.E Hinton, "Training products of experts by minimizing contrastive divergence," Neural Computation, vol. 14, no. 8, pp. 1711-1800, 2002.
9. G.E. Hinton and R. Salakhutdinov, "Reducing the dimensionality of data with neural networks," Science, vol. 313, no. 5786, pp. 504-507, 2006.
10. A. Mohamed, G.E. Dahl, and G.E. Hinton, "Acoustic modeling using deep belief networks," IEEE Trans. Speech Audio Process., vol. 20, no. 1, pp. 14-22, 2012.
11. G.E. Dahl, D. Yu, L. Deng, and A. Acero, "Context-dependent pre-trained deep neural networks for large-vocabulary speech recognition," IEEE Trans. Speech Audio Process., vol. 20, no. 1, pp. 30-42, 2012.
12. G.E. Hinton, L. Deng, D. Yu, G. Dahl, A. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. Andrew Sainath, and B. Kingsbury, "Deep neural networks for acoustic modeling in speech recognition," IEEE Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012.
13. L. Deng, M. Seltzer, D. Yu, A. Acero, A. Mohamed, and G.E. Hinton, "Binary coding of speech spectrograms using a deep auto-encoder," in Interspeech, 2010, pp. 1692-1695.
14. B. Uria, S. Renals, and K. Richmond, "A deep neural network for acoustic-articulatory speech inversion," in NIPS 2011 Workshop on Deep Learning and Unsupervised Feature Learning, 2011.
15. G.E Hinton, S. Osindero, and Y.W. Teh, "A fast learning algorithm for deep belief nets," Neural Computation, vol. 18, no. 7, pp. 1527-1554, 2006.