Details of the Nitech HMM-Based Speech Synthesis System for the Blizzard Challenge 2005

In January 2005, an open evaluation of corpus-based text-to-speech synthesis systems using common speech datasets, named Blizzard Challenge 2005, was conducted. Nitech group participated in this challenge, entering an HMM-based speech synthesis system called Nitech-HTS 2005. This paper describes the technical details, building processes, and performance of our system. We first give an overview of the basic HMM-based speech synthesis system, and then describe new features integrated into Nitech-HTS 2005 such as STRAIGHT-based vocoding, HSMM-based acoustic modeling, and a speech parameter generation algorithm considering GV. Constructed Nitech-HTS 2005 voices can generate speech waveforms at 0.3 ×RT (real-time ratio) on a 1.6 GHz Pentium 4 machine, and footprints of these voices are less than 2 Mbytes. Subjective listening tests showed that the naturalness and intelligibility of the Nitech-HTS 2005 voices were much better than expected.

[1]  Keiichi Tokuda,et al.  Investigation of State Duration Model based on Gamma distribution for HMM-based Speech Synthesis , 2001 .

[2]  Hideki Kawahara,et al.  Aperiodicity extraction and control using mixed mode excitation and group delay manipulation for a high quality speech analysis, modification and synthesis system STRAIGHT , 2001, MAVEBA.

[3]  Keiichi Tokuda,et al.  A Speech Parameter Generation Algorithm Considering Global Variance for HMM-Based Speech Synthesis , 2007, IEICE Trans. Inf. Syst..

[4]  Keiichi Tokuda,et al.  Speaker interpolation in HMM-based speech synthesis system , 1997, EUROSPEECH.

[5]  Keiichi Tokuda,et al.  Adaptation of pitch and spectrum for HMM-based speech synthesis using MLLR , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[6]  Alan W. Black,et al.  Perfect synthesis for all of the people all of the time , 2002, Proceedings of 2002 IEEE Workshop on Speech Synthesis, 2002..

[7]  Mari Ostendorf,et al.  The impact of speech recognition on speech synthesis , 2002, Proceedings of 2002 IEEE Workshop on Speech Synthesis, 2002..

[8]  Keiichi Tokuda,et al.  Incorporating a mixed excitation model and postfilter into HMM-based text-to-speech synthesis , 2005, Systems and Computers in Japan.

[9]  Keiichi Tokuda,et al.  Simultaneous modeling of spectrum, pitch and duration in HMM-based speech synthesis , 1999, EUROSPEECH.

[10]  Keiichi Tokuda,et al.  An adaptive algorithm for mel-cepstral analysis of speech , 1992, [Proceedings] ICASSP-92: 1992 IEEE International Conference on Acoustics, Speech, and Signal Processing.

[11]  K. Tokuda,et al.  Speech parameter generation from HMM using dynamic features , 1995, 1995 International Conference on Acoustics, Speech, and Signal Processing.

[12]  Satoshi Imai,et al.  Cepstral analysis synthesis on the mel frequency scale , 1983, ICASSP.

[13]  Heiga Zen,et al.  An introduction of trajectory model into HMM-based speech synthesis , 2004, SSW.

[14]  Keiichi Tokuda,et al.  Speech parameter generation algorithms for HMM-based speech synthesis , 2000, 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100).

[15]  Eric Moulines,et al.  Pitch-synchronous waveform processing techniques for text-to-speech synthesis using diphones , 1989, Speech Commun..

[16]  Roy D. Patterson,et al.  Fixed point analysis of frequency to instantaneous frequency mapping for accurate estimation of F0 and periodicity , 1999, EUROSPEECH.

[17]  Hideki Kawahara,et al.  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 , 1999, Speech Commun..

[18]  Jj Odell,et al.  The Use of Context in Large Vocabulary Speech Recognition , 1995 .

[19]  Keiichi Tokuda,et al.  Eigenvoices for HMM-based speech synthesis , 2002, INTERSPEECH.

[20]  Paul Taylor,et al.  Festival Speech Synthesis System , 1998 .

[21]  Keiichi Tokuda,et al.  Hidden Markov models based on multi-space probability distribution for pitch pattern modeling , 1999, 1999 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings. ICASSP99 (Cat. No.99CH36258).

[22]  Alan V. Oppenheim,et al.  Discrete representation of signals , 1972 .

[23]  Heiga Zen,et al.  Hidden semi-Markov model based speech synthesis , 2004, INTERSPEECH.

[24]  Stephen E. Levinson,et al.  Continuously variable duration hidden Markov models for automatic speech recognition , 1986 .

[25]  Keiichi Tokuda,et al.  The blizzard challenge - 2005: evaluating corpus-based speech synthesis on common datasets , 2005, INTERSPEECH.

[26]  Ren-Hua Wang,et al.  Minimum Generation Error Training for HMM-Based Speech Synthesis , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[27]  Junichi Yamagishi,et al.  Average-Voice-Based Speech Synthesis , 2006 .

[28]  Christina L. Bennett.  Large scale evaluation of corpus-based synthesizers: results and lessons from the blizzard challenge 2005 , 2005, INTERSPEECH.

[29]  David Yarowsky,et al.  A corpus-based synthesizer , 1992, ICSLP.

[30]  Keiichi Tokuda,et al.  Incorporating a mixed excitation model and postfilter into HMM-based text-to-speech synthesis , 2005 .

[31]  Martine Grice,et al.  The SUS test: A method for the assessment of text-to-speech synthesis intelligibility using Semantically Unpredictable Sentences , 1996, Speech Commun..

[32]  Alan W. Black.  Unit selection and emotional speech , 2003, INTERSPEECH.