Analysis of spectral enhancement using global variance in HMM-based speech synthesis

Proceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH

Volumn , Issue , 2014, Pages 2917-2921

  Nose, Takashi ^a   Ito, Akinori ^a  

^a TOHOKU UNIVERSITY   (Japan)

Author keywords

Global variance; HMM based speech synthesis; Over smoothing; Parameter generation; Variance compensation

Indexed keywords

SPEECH SYNTHESIS;

GLOBAL VARIANCE; HMM-BASED SPEECH SYNTHESIS; OVER-SMOOTHING; PARAMETER GENERATION; VARIANCE COMPENSATIONS;

SPEECH COMMUNICATION;

EID: 84910088495     PISSN: 2308457X     EISSN: 19909772     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (24)

1. H. Zen, K. Tokuda, and A. Black, "Statistical parametric speech synthesis, " Speech Communication, vol. 51, no. 11, pp. 1039-1064, 2009.
2. T. Nose and T. Kobayashi, "Recent development of HMM-based expressive speech synthesis and its applications, " in Proc. APSIPA ASC 2011, 2011, http://www.apsipa.org/proceedings2011/pdf/APSIPA189.pdf.
3. T. Yoshimura, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, "Mixed excitation for HMM-based speech synthesis, " in Proc. EUROSPEECH 2001, vol. 3, 2001, pp. 2263-2266.
4. 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.
5. S. Tiomkin, D. Malah, and S. Shechtman, "Statistical textto-speech synthesis based on segment-wise representation with a norm constraint, " IEEE Trans. Audio, Speech, and Language Process., vol. 18, no. 5, pp. 1077-1082, 2010.
6. Y. Ohtani, M. Tamura, M. Morita, T. Kagoshima, and M. Akamine, "Histogram-based spectral equalization for HMM-based speech synthesis using mel-LSP, " in Proc. INTERSPEECH 2012, 2012, pp. 1155-1158.
7. Y. Wu, H. Zen, Y. Nankaku, and K. Tokuda, "Minimum generation error criterion considering global/local variance for HMM-based speech synthesis, " in Proc. ICASSP 2008, 2008, pp. 4621-4624.
8. T. Toda and S. Young, "Trajectory training considering global variance for HMM-based speech synthesis, " in Proc. ICASSP 2009, 2009, pp. 4025-4028.
9. Z. Ling, Y. Hu, and L. Dai, "Global variance modeling on the log power spectrum of LSPs for HMM-based speech synthesis, " in Proc. INTERSPEECH 2010, 2010, pp. 825-828.
10. S. Pan, Y. Nankaku, K. Tokuda, and J. Tao, "Global variance modeling on frequency domain delta LSP for HMMbased speech synthesis, " in Proc. ICASSP 2011, 2011, pp. 4716-4719.
11. H. Zen, M. Gales, Y. Nankaku, and K. Tokuda, "Product of experts for statistical parametric speech synthesis, " IEEE Trans. Audio, Speech, and Language Process., vol. 20, no. 3, pp. 794-805, 2012.
12. Y. Wu and R. Wang, "Minimum generation error training for HMM-based speech synthesis, " in Proc. ICASSP 2006, 2006, pp. 889-892.
13. H. Zen, K. Tokuda, and T. Kitamura, "Reformulating the HMM as a trajectory model by imposing explicit relationships between static and dynamic feature vector sequences, " Computer Speech & Language, vol. 21, no. 1, pp. 153-173, 2007.
14. T. Nose, V. Chunwijitra, and T. Kobayashi, "A parameter generation algorithm using local variance for HMM-based speech synthesis, " IEEE Trans. Audio, Speech, and Language Process., pp. 221-228, 2014.
15. K. Tokuda, T. Kobayashi, and S. Imai, "Speech parameter generation from HMM using dynamic features, " in Proc. ICASSP-95, 1995, pp. 660-663.
16. M. Shannon and W. Byrne, "Fast, low-artifact speech synthesis considering global variance, " in Proc. ICASSP 2013, 2013, pp. 7869-7873.
17. H. Benisty and D. Malah, "Voice conversion using GMM with enhanced global variance, " in INTERSPEECH 2011, 2011, pp. 669-672.
18. Z.-H. Ling and L.-R. Dai, "Minimum Kullback-Leibler divergence parameter generation for HMM-based speech synthesis, " IEEE Trans. Audio, Speech, and Language Process., vol. 20, no. 5, pp. 1492-1502, 2012.
19. 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 Communication, vol. 9, no. 4, pp. 357-363, 1990.
20. H. Kawahara, I. Masuda-Katsuse, and A. deCheveigne, "Restructuring speech representations using a pitchadaptive time-frequency smoothing and an instantaneousfrequency-based F0 extraction: Possible role of a repetitive structure in sounds, " Speech Communication, vol. 27, no. 3-4, pp. 187-207, 1999.
21. H. Zen, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, "A hidden semi-Markov model-based speech synthesis system, " IEICE Trans. Inf. & Syst., vol. E90-D, no. 5, pp. 825-834, 2007.
22. K. Shinoda and T. Watanabe, "MDL-based contextdependent subword modeling for speech recognition, " J. Acoust. Soc. Jpn. (E), vol. 21, no. 2, pp. 79-86, 2000.
23. H. Zen, A. Senior, and M. Schuster, "Statistical parametric speech synthesis using deep neural networks, " in Proc. ICASSP 2013, 2013, pp. 7962-7966.
24. T. Koriyama, T. Nose, and T. Kobayashi, "Statistical parametric speech synthesis based on Gaussian process regression, " IEEE Trans. Audio, Speech, and Language Process., pp. 173-183, 2013.