An intuitive style control technique in HMM-based expressive speech synthesis using subjective style intensity and multiple-regression global variance model

Speech Communication

Volumn 55, Issue 2, 2013, Pages 347-357

  Nose, Takashi ^a   Kobayashi, Takao ^a  

^a TOKYO INSTITUTE OF TECHNOLOGY   (Japan)

Author keywords

HMM based expressive speech synthesis; Multiple regression global variance model; Multiple regression HSMM; Style control; Style intensity

Indexed keywords

COMPENSATION METHOD; CONTROL TECHNIQUES; EMOTIONAL EXPRESSIONS; EXPRESSIVE SPEECH SYNTHESIS; HIDDEN SEMI-MARKOV MODELS; MODEL PARAMETERS; MODEL TRAINING; MULTIPLE-REGRESSION HSMM; NATURAL SPEECH; SPEAKING STYLES; SPEECH SYNTHESIS SYSTEM; STYLE INTENSITY; SYNTHETIC SPEECH; TRAINING DATA; VARIANCE MODELS;

HIDDEN MARKOV MODELS; REGRESSION ANALYSIS;

SPEECH SYNTHESIS;

EID: 84870246600     PISSN: 01676393     EISSN: None     Source Type: Journal    
DOI: 10.1016/j.specom.2012.09.003     Document Type: Article

References (29)

1. Anastasakos, T.; McDonough, J.; Schwartz, R.; Makhoul, J.; 1996. A compact model for speaker adaptive training. In: Proc. ICSLP-96, pp. 1137-1140.
2. R. Cowie, and R.R. Cornelius Describing the emotional states that are expressed in speech Speech Comm. 40 1-2 2003 5 32
3. D. Erickson Expressive speech: production, perception and application to speech synthesis Acoust. Sci. Tech. 26 4 2005 317 325
4. Fujinaga, K.; Nakai, M.; Shimodaira, H.; Sagayama, S.; 2001. Multiple-regression hidden Markov model. In: Proc. ICASSP 2001, pp. 513-516.
5. M. Gales Maximum likelihood linear transformations for HMM-based speech recognition Comput. Speech Language 12 1998 75 98
6. M. Gales Cluster adaptive training of hidden Markov models IEEE Trans. Speech Audio Process 8 4 2000 417 428
7. A. Iida, N. Campbell, F. Higuchi, and M. Yasumura A corpus-based speech synthesis system with emotion Speech Comm. 40 1-2 2003 161 187
8. H. Kawahara, I. Masuda-Katsuse, and A. de Cheveigne 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 Comm. 27 3-4 1999 187 207
9. Koriyama, T.; Nose, T.; Kobayashi, T.; 2011. On the use of extended context for HMM-based spontaneous conversational speech synthesis. In: Proc. INTERSPEECH 2011, pp. 2657-2660.
10. 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 Comm. 9 4 1990 357 363
11. Miyanaga, K.; Masuko, T.; Kobayashi, T.; 2004. A style control technique for HMM-based speech synthesis. In: Proc. INTERSPEECH 2004-ICSLP, pp. 1437-1440.
12. N. Niwase, J. Yamagishi, and T. Kobayashi Human walking motion synthesis with desired pace and stride length based on HSMM IEICE Trans. Inf. Syst. E88-D 11 2005 2492 2499
13. T. Nose, M. Tachibana, and T. Kobayashi HMM-based style control for expressive speech synthesis with arbitrary speaker's voice using model adaptation IEICE Trans. Inf. Syst. E92-D 3 2009 489 497
14. T. Nose, J. Yamagishi, T. Masuko, and T. Kobayashi A style control technique for HMM-based expressive speech synthesis IEICE Trans. Inf. Syst. E90-D 9 2007 1406 1413
15. J.F. Pitrelli, R. Bakis, E.M. Eide, R. Fernandez, W. Hamza, and M.A. Picheny The IBM expressive text-to-speech synthesis system for American English IEEE Trans. Audio Speech Language Process. 14 4 2006 1099 1108
16. K. Scherer, and J. Oshinsky Cue utilization in emotion attribution from auditory stimuli Motivat. Emot. 1 4 1977 331 346
17. Schröder, M.; 2001. Emotional speech synthesis: a review. In: Proc. EUROSPEECH 2001, pp. 561-564.
18. Schuller, B.; Rigoll, G.; Lang, M.; 2003. Hidden Markov model-based speech emotion recognition. In: Proc. ICASSP 2003, vol. 1, pp. 401-404.
19. K. Shinoda, and T. Watanabe MDL-based context-dependent subword modeling for speech recognition J. Acoust. Soc. Jpn. (E) 21 2 2000 79 86
20. M. Tachibana, J. Yamagishi, T. Masuko, and T. Kobayashi Speech synthesis with various emotional expressions and speaking styles by style interpolation and morphing IEICE Trans. Inf. Syst. E88-D 11 2005 2484 2491
21. T. Toda, and K. Tokuda A speech parameter generation algorithm considering global variance for HMM-based speech synthesis IEICE Trans. Inf. Syst. E90-D 5 2007 816 824
22. Tsuzuki, R.; Zen, H.; Tokuda, K.; Kitamura, T.; Bulut, M.; Narayanan, S.; 2004. Constructing emotional speech synthesizers with limited speech database. In: Proc. INTERSPEECH 2004-ICSLP, pp. 1185-1188.
23. Yamagishi, J.; Onishi, K.; Masuko, T.; Kobayashi, T.; 2003a. Modeling of various speaking styles and emotions for HMM-based speech synthesis. In: Proc. INTERSPEECH 2003-EUROSPEECH, pp. 2461-2464.
24. J. Yamagishi, M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi A context clustering technique for average voice models IEICE Trans. Inf. Syst. E86-D 3 2003 534 542
25. J. Yamagishi, M. Tamura, T. Masuko, K. Tokuda, and T. Kobayashi A training method of average voice model for HMM-based speech synthesis IEICE Trans. Fundamentals E86-A 8 2003 1956 1963
26. Yamagishi, J.; Zen, H.; Wu, Y.; Toda, T.; Tokuda, K.; 2008. The HTS-2008 system: yet another evaluation of the speaker-adaptive HMM-based speech synthesis system in the 2008 blizzard challenge.
27. Yoshimura, T.; Tokuda, K.; Masuko, T.; Kobayashi, T.; Kitamura, T.; 1999. Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis. In: Proc. EUROSPEECH, pp. 2347-2350.
28. K. Yu, H. Zen, F. Mairesse, and S. Young Context adaptive training with factorized decision trees for HMM-based statistical parametric speech synthesis Speech Comm. 53 6 2011 914 923
29. H. Zen, K. Tokuda, and A. Black Statistical parametric speech synthesis Speech Comm. 51 11 2009 1039 1064