The HMM-based Speech Synthesis System (HTS) Version 2.0

6th ISCA Workshop on Speech Synthesis, SSW 2007

Volumn , Issue , 2007, Pages 294-299

  Zen, Heiga ^a   Nose, Takashi ^b   Yamagishi, Junichi ^b,c   Sako, Shinji ^a,d   Masuko, Takashi ^b   Black, Alan W ^e   Tokuda, Keiichi ^a  

^a NAGOYA INSTITUTE OF TECHNOLOGY   (Japan)

^b TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

^c UNIVERSITY OF EDINBURGH   (United Kingdom)

^d UNIVERSITY OF TOKYO   (Japan)

^e CARNEGIE MELLON UNIVERSITY   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

OPEN SOURCE SOFTWARE; OPEN SYSTEMS; SPEECH SYNTHESIS;

CONTEXT DEPENDENT; HIDDEN MARKOV MODEL-BASED SPEECH SYNTHESIS; HIDDEN-MARKOV MODELS; MODELING SPECTRA; OPEN-SOURCE SOFTWARES; RESEARCH PLATFORMS; SOFTWARE TOOLKITS; SPEECH SYNTHESIS SYSTEM; SPEECH WAVEFORMS; STATISTICAL PARAMETRIC SPEECH SYNTHESIS;

HIDDEN MARKOV MODELS;

EID: 85133720638     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (83)

1. A.W. Black and P. Taylor, “CHATR: a generic speech synthesis system,” in Proc. COLING94, 1994.
2. A. Hunt and A.W. Black, “Unit selection in a concatenative speech synthesis system using a large speech database,” in Proc. ICASSP, 1996, pp. 373–376.
3. R.E. Donovan and P.C. Woodland, “Automatic speech synthesizer parameter estimation using HMMs,” in Proc. ICASSP, 1995, pp. 640–643.
4. E. Eide, A. Aaron, R. Bakis, W. Hamza, M. Picheny, and J. Pitrelli, “A corpus-based approach to expressive speech synthesis,” in Proc. ISCA SSW5, 2004.
5. A.W. Black, “Unit selection and emotional speech,” in Proc. Eurospeech, 2003, pp. 1649–1652.
6. 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.
7. 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, Jan. 2007.
8. 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.
9. A.W. Black, H. Zen, and K. Tokuda, “Statistical parametric speech synthesis,” in Proc. ICASSP, 2007, pp. 1229–1232.
10. J. Yu, M. Zhang, J. Tao, and X. Wang, “A novel HMM-based TTS system using both continuous HMMs and discrete HMMs,” in Proc. ICASSP, 2007, pp. 709–712.
11. T. Fukada, K. Tokuda, Kobayashi T., and S. Imai, “An adaptive algorithm for mel-cepstral analysis of speech,” in Proc. ICASSP, 1992, pp. 137–140.
12. K. Tokuda, T. Masuko, N. Miyazaki, and T. Kobayashi, “Hidden Markov models based on multi-space probability distribution for pitch pattern modeling,” in Proc. ICASSP, 1999, pp. 229–232.
13. T. Yoshimura, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, “Duration modeling for HMM-based speech synthesis,” in Proc. ICSLP, 1998, pp. 29–32.
14. H. Zen, T. Masuko, T. Yoshimura, K. Tokuda, T. Kobayashi, and T. Kitamura, “State duration modeling for HMM-based speech synthesis,” IEICE Trans. on Inf. & Syst., vol. E90-D, no. 3, pp. 692–693, 2007.
15. K. Tokuda, T. Yoshimura, T. Masuko, T. Kobayashi, and T. Kitamura, “Speech parameter generation algorithms for HMM-based speech synthesis,” in Proc. ICASSP, 2000, pp. 1315–1318.
16. S. Imai, “Cepstral analysis synthesis on the mel frequency scale,” in Proc. ICASSP, 1983, pp. 93–96.
17. T. Masuko, K. Tokuda, T. Kobayashi, and S. Imai, “Voice characteristics conversion for HMM-based speech synthesis system,” in Proc. ICASSP, 1997, pp. 1611–1614.
18. 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.
19. T. Yoshimura, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, “Speaker interpolation in HMM-based speech synthesis system,” in Proc. Eurospeech, 1997, pp. 2523–2526.
20. M. Tachibana, J. Yamagishi, T. Masuko, and T. Kobayashi, “Speech synthesis with various emotional expressions and speaking styles by style interpolationand morphing,” IEICE Trans. Inf. & Syst., vol. E88-D, no. 11, pp. 2484–2491, 2005.
21. K. Shichiri, A. Sawabe, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, “Eigenvoices for HMM-based speech synthesis,” in Proc. ICSLP, 2002, pp. 1269–1272.
22. T. Nose, J. Yamagishi, and T. Kobayashi, “A style control technique for speech synthesis using multiple regression HSMM,” in Proc. Interspeech, 2006, pp. 1324–1327.
23. K. Tokuda, H. Zen, J. Yamagishi, T. Masuko, S. Sako, A.W. Black, and T. Nose, “The HMM-based speech synthesis system (HTS),” http://hts.sp.nitech.ac.jp/.
24. S. Young, G. Evermann, M. Gales, T. Hain, D. Kershaw, X.-Y. Liu, G. Moore, J. Odell, D. Ollason, D. Povey, V. Valtchev, and P. Woodland, The Hidden Markov Model Toolkit (HTK) version 3.4, 2006, http://htk.eng.cam.ac.uk/.
25. K. Shinoda and T. Watanabe, “Acoustic modeling based on the MDL criterion for speech recognition,” in Proc. Eurospeech, 1997, pp. 99–102.
26. A.W. Black, P. Taylor, and R. Caley, “The festival speech synthesis system,” http://www.festvox.org/festival/.
27. J. Kominek and A.W. Black, “CMU ARCTIC databases for speech synthesis,” Tech. Rep. CMU-LTI-03-177, Carnegie Mellon University, 2003.
28. T. Yoshimura, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, “Incorporation of mixed excitation model and postfilter into HMM-based text-to-speech synthesis,” IEICE Trans. Inf. & Syst. (Japanese Edition), vol. J87-D-II, no. 8, pp. 1563–1571, Aug. 2004.
29. Galatea Project, “Galatea – An open-source toolkit for anthropomorphic spoken dialogue agent,” http://hil.t.u-tokyo.ac.jp/galatea/.
30. H. Kawai, T. Toda, J. Yamagishi, T. Hirai, J. Ni, T. Nishizawa, M. Tsuzaki, and K. Tokuda, “XIMERA: A concatenative speech synthesis system with large scale corpora,” IEICE Trans. Inf. & Syst. (Japanese Edition), vol. J89-D, no. 12, pp. 2688–2698, Dec. 2006.
31. S. Krstulovic, A. Hunecke, and M. Schroeder, “An HMM-based speech synthesis system applied to German and its adaptation to a limited set of expressive football announcements,” in Proc. of Interspeech, 2007.
32. M. Schröder and J. Trouvain, “The German text-to-speech synthesis system MARY: A tool for research, development and teaching,” InternationalJournal of Speech Technology, vol. 6, pp. 365–377, 2003.
33. Y.-J. Wu and R.H. Wang, “HMM-basedtrainable speech synthesis for Chinese,” Journal of Chinese InformationProcessing, vol. 20, no. 4, pp. 75–81, 2006.
34. Y. Qian, F. Soong, Y. Chen, and M. Chu, “An HMM-based Mandarin Chinese text-to-speech system,” in Proc. of ISCSLP, 2006.
35. S.-J. Kim, J.-J. Kim, and M.-S. Hahn, “Implementationand evaluation of an HMM-based Korean speech synthesis system,” IEICE Trans. Inf. & Syst., vol. E89-D, pp. 1116–1119, 2006.
36. C. Weiss, R. Maia, K. Tokuda, and W. Hess, “Low resource HMM-based speech synthesis applied to German,” in ESSP, 2005.
37. M. Barros, R. Maia, K. Tokuda, D. Freitas, and F. Resende Jr., “HMM-based European Portuguese speech synthesis,” in Interspeech, 2005, pp. 2581–2584.
38. A. Lundgren, An HMM-based text-to-speech system applied to Swedish, Master thesis, Royal Institute of Technology (KTH), 2005.
39. T. Ojala, Auditory quality evaluation of present Finnish text-to-speech systems, Master thesis, Helsinki University of Technology, 2006.
40. M. Vainio, A. Suni, and P. Sirjola, “Developing a Finnish concept-to-speech system,” in 2nd Baltic conference on HLT, 2005, pp. 201–206.
41. B. Vesnicer and F. Mihelic, “Evaluation of the Slovenian HMM-based speech synthesis system,” in TSD, 2004, pp. 513–520.
42. S. Martincic-Ipsic and I. Ipsic, “Croatian HMM-based speech synthesis,” Journal of Computing and Information Technology, vol. 14, no. 4, pp. 307–313, 2006.
43. O. Abdel-Hamid,S. Abdou, and M. Rashwan, “Improving Arabic HMM based speech synthesis quality,” in Interspeech, 2006, pp. 1332–1335.
44. J. Latorre, K. Iwano, and S. Furui, “Polyglot synthesis using a mixture of monolingual corpora,” in ICASSP, 2005, vol. 1, pp. 1–4.
45. X. Gonzalvo, I. Iriondo, J. Socor, F. Alas, and C. Monzo, “HMM-based Spanish speech synthesis using CBR as F0 estimator,” in ITRW on NOLISP, 2007.
46. S. Chomphan and T. Kobayashi, “Implementationand evaluation of an HMM-based Thai speech synthesis system,” in Proc. of Interspeech, 2007.
47. D. Huggins-Daines and A. Rudnicky, “A constrained Baum-Welch algorithm for improved phoneme segmentation and efficient training,” in Proc. of Interspeech, 2006, pp. 1205–1208.
48. K. Tokuda, H. Zen, and T. Kitamura, “Reformulating the HMM as a trajectory model,” in Proc. Beyond HMM – Workshop on statisticalmodeling approach for speech recognition, 2004.
49. M.J.F. Gales, “Maximum likelihood linear transformations for HMM-based speech recognition,” Computer Speech & Language, vol. 12, no. 2, pp. 75–98, 1998.
50. M.J.F. Gales, “Semi-tied covariance matrices for hidden Markov models,” IEEE Transactions on Speech and Audio Processing, vol. 7, no. 3, pp. 272–281, 1999.
51. M.J.F. Gales, “Maximum likelihood multiple projection schemes for hidden Markov models,” IEEE Trans. Speech & Audio Process., vol. 10, no. 2, pp. 37–47, 2002.
52. J. Yamagishi, M. Tachibana, T. Masuko, and T. Kobayashi, “Speaking style adaptation using context clustering decision tree for HMM-based speech synthesis,” in Proc. ICASSP, 2004, pp. 5–8.
53. M. Tachibana, J. Yamagishi, T. Masuko, and T. Kobayashi, “A style adaptation technique for speech synthesis using HSMM and suprasegmental features,” IEICE Trans. Inf. & Syst., vol. E89-D, no. 3, pp. 1092–1099, 2006.
54. J.L. Gauvain and C.-H. Lee, “Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains,” IEEE Trans. on Speech & Audio Process., vol. 2, no. 2, pp. 291–298, 1994.
55. J. Yamagishi and T. Kobayashi, “Average-voice-based speech synthesis using HSMM-based speaker adaptation and adaptive training,” IEICE Trans. Inf. & Syst., vol. E90-D, no. 2, pp. 533–543, 2007.
56. J. Yamagishi, K. Ogata, Y. Nakano, J. Isogai, and T. Kobayashi, “HSMM-based model adaptation algorithms for average-voice-based speech synthesis,” in Proc. ICASSP, 2006, pp. 77–80.
57. J. Yamagishi, Average-Voice-BasedSpeech Synthesis, Ph.D. thesis, Tokyo Instituteof Technology, 2006.
58. T. Toda and K. Tokuda, “A speech parameter generationalgorithm considering global variance for HMM-based speech synthesis,” IEICE Trans. Inf. & Syst., vol. E90-D, no. 5, pp. 816–824, 2007.
59. M.J.F. Gales, “The generation and use of regression class trees for MLLR adaptation,” Tech. Rep. CUED/F-INFENG/TR263, Cambridge University, 1996.
60. M. Ostendorf and I. Bulyko, “The impact of speech recognition on speech synthesis,” in Proc. the IEEE Workshop on Speech Synthesis, 2002, CD-ROM proceeding.
61. K. Mori, Y. Nankaku, C. Miyajima, K. Tokuda, and T. Kitamura, “Motion generation for Japanese finger language based on hidden Markov models,” in Proc. FIT, 2005, vol. 3, pp. 569–570, (in Japanese).
62. N. Niwase, J. Yamagishi, and T. Kobayashi, “Human walking motion synthesis with desired pace and stride length based on HSMM,” IEICE Trans. Inf. & Syst., vol. E88-D, no. 11, pp. 2492–2499, 2005.
63. G. Hofer, H. Shimodaira, and J. Yamagishi, “Speech driven head motion synthesis based on a trajectory model,” in Proc. SIG-GRAPH, 2007, (submitted).
64. O. Govokhina, G. Bailly, G. Breton, and P. Bagshaw, “TDA: a new trainable trajectory formation system for facial animation,” in Proc. Interspeech, 2006, pp. 1274–1247.
65. M. Tamura, S. Kondo, T. Masuko, and T. Kobayashi, “Text-to-audio-visualspeechsynthesisbasedon parametergenerationfrom HMM,” in Proc. Eurospeech, 1999, pp. 959–962.
66. S. Sako, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, “HMM-based text-to-audio-visualspeech synthesis,” in Proc. ICSLP, 2000, pp. 25–28.
67. T. Ishikawa, Y. Sawada, H. Zen, Y. Nankaku, C. Miyajima, K. Tokuda, and T. Kitamura, “Audio-visuallarge vocabulary continuous speech recognition based on early integration,” in Proc. FIT, 2002, pp. 203–204, (in Japanese).
68. K. Richmond, “A trajectory mixture density network for the acoustic-articulatoryinversion mapping,” in Proc. of Interspeech, 2006, pp. 577–580.
69. K. Emoto, H. Zen, K. Tokuda, and T. Kitamura, “Accent type recognition for automatic prosodic labeling,” in Proc. Autumn Meeting of ASJ, 2003, vol. I, pp. 225–226, (in Japanese).
70. H.-L. Wang, Y. Qian, F.K. Soong, J.-L. Zhou, and J.-Q. Han, “A multi-space distribution (MSD) approach to speech recognition of tonal languages,” in Proc. of Interspeech, 2006, pp. 125–128.
71. T. Hoshiya, S. Sako, H. Zen, K. Tokuda, T. Masuko, T. Kobayashi, and T. Kitamura, “Improving the performance of HMM-based very low bitrate speech coding,” in Proc. ICASSP, 2003, vol. 1, pp. 800–803.
72. K. Tanaka, S. Kuroiwa, S. Tsuge, and F. Ren, “An acoustic model adaptationusing HMM-based speech synthesis,” in Proc. NLPKE, 2003, vol. 1, pp. 368–373.
73. M. Ishihara, C. Miyajima, N. Kitaoka, K. Itou, and K. Takeda, “An approach for training acoustic models based on the vocabulary of the target speech recognition task,” in Proc. Spring Meeting of ASJ, 2007, pp. 153–154, (in Japanese).
74. R. Terashima, T. Yoshimura, T. Wakita, K. Tokuda, and T. Kitamura, “An evaluation method of ASR performance by HMM-based speech synthesis,” in Proc. Spring Meeting of ASJ, 2003, pp. 159–160, (in Japanese).
75. L. Ma, Y.-J. Wu, P. Liu, and F. Soong, “A MSD-HMM approach to pen trajectory modeling for online handwriting recognition,” in Proc. ICDAR, 2007.
76. 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.
77. Y. Nankaku, H. Zen, K. Tokuda, T. Kitamura, and T. Masuko, “A Bayesian approach to HMM-based speech synthesis,” in Tech. rep. of IEICE, 2003, vol. 103, pp. 19–24, (in Japanese).
78. 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, 2006.
79. J. Yamagishi, M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi, “A context clustering technique for average voice models,” IEICE Trans. Inf. & Syst., vol. E86-D, no. 3, pp. 534–542, 2003.
80. J. Isogai, J. Yamagishi, and T. Kobayashi, “Model adaptation and adaptive training using ESAT algorithm for HMM-based speech synthesis,” in Proc. Interspeech, 2005, pp. 2597–2600.
81. Y. Nakano, M. Tachibana, J. Yamagishi, and T. Kobayashi, “Constrained structural maximum a posteriori linear regression for average-voice-based speech synthesis,” in Proc. Interspeech, 2006, pp. 2286–2289.
82. 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 Communication, vol. 27, pp. 187–207, 1999.
83. H. Zen, T. Toda, and K. Tokuda, “The Nitech-NAIST HMM-based speech synthesis system for the Blizzard Challenge 2006,” in Blizzard Challenge Workshop, 2006.