Eigenvoice-Based Approach to Voice Conversion and Voice Quality Control

This paper reviews our proposed approach to voice conversion (VC) and voice quality control based on an eigenvoice technique. VC is a technique to modify nonlinguistic information such as speaker individuality while keeping linguistic information unchanged. In the traditional VC framework, a conversion model for a source and target speaker-pair needs to be trained in advance using a parallel data set consisting of utterance-pairs of these two speakers. To make VC technologies more practical, we have developed a new VC paradigm for flexibly building the conversion model for an arbitrary speaker-pair by effectively using speech samples of many other speakers. In this paper, we give an overview of eigenvoice conversion (EVC) as one of our proposed VC techniques.

[1]  Richard M. Schwartz,et al.  A compact model for speaker-adaptive training , 1996, Proceeding of Fourth International Conference on Spoken Language Processing. ICSLP '96.

[2]  Tomoki Toda,et al.  An improved one-to-many eigenvoice conversion system , 2008, INTERSPEECH.

[3]  Keiichi Tokuda,et al.  Statistical mapping between articulatory movements and acoustic spectrum using a Gaussian mixture model , 2008, Speech Commun..

[4]  Chin-Hui Lee,et al.  Maximum a posteriori estimation for multivariate Gaussian mixture observations of Markov chains , 1994, IEEE Trans. Speech Audio Process..

[5]  K. Shichiri.  Eigenvoice for HMM-based speech synthesis , 2002 .

[6]  Kiyohiro Shikano,et al.  Non-Audible Murmur (NAM) Recognition , 2006, IEICE Trans. Inf. Syst..

[7]  Jia Liu,et al.  Voice conversion with smoothed GMM and MAP adaptation , 2003, INTERSPEECH.

[8]  Alexander Kain,et al.  Spectral voice conversion for text-to-speech synthesis , 1998, Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181).

[9]  Vassilios Diakoloukas,et al.  Maximum-likelihood stochastic-transformation adaptation of hidden Markov models , 1999, IEEE Trans. Speech Audio Process..

[10]  Hazarathaiah Malepati,et al.  Speech and Audio Processing , 2010 .

[11]  Hermann Ney,et al.  Text-Independent Voice Conversion Based on Unit Selection , 2006, 2006 IEEE International Conference on Acoustics Speech and Signal Processing Proceedings.

[12]  Roland Kuhn,et al.  Rapid speaker adaptation in eigenvoice space , 2000, IEEE Trans. Speech Audio Process..

[13]  K. Shikano,et al.  Statistical analysis of bilingual speaker's speech for cross-language voice conversion. , 1991, The Journal of the Acoustical Society of America.

[14]  Chung-Hsien Wu,et al.  Map-based adaptation for speech conversion using adaptation data selection and non-parallel training , 2006, INTERSPEECH.

[15]  Yoshinori Sagisaka,et al.  Speech spectrum conversion based on speaker interpolation and multi-functional representation with weighting by radial basis function networks , 1995, Speech Commun..

[16]  Douglas D. O'Shaughnessy,et al.  Statistical recovery of wideband speech from narrowband speech , 1992, IEEE Trans. Speech Audio Process..

[17]  Tomoki Toda,et al.  An evaluation of many-to-one voice conversion algorithms with pre-stored speaker data sets , 2007, SSW.

[18]  Tomoki Toda,et al.  Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory , 2007, IEEE Transactions on Audio, Speech, and Language Processing.

[19]  Tomoki Toda,et al.  Eigenvoice conversion based on Gaussian mixture model , 2006, INTERSPEECH.

[20]  Eric Moulines,et al.  Continuous probabilistic transform for voice conversion , 1998, IEEE Trans. Speech Audio Process..

[21]  Zicheng Liu,et al.  Multisensory processing for speech enhancement and magnitude-normalized spectra for speech modeling , 2008, Speech Commun..

[22]  John-Paul Hosom,et al.  Improving the intelligibility of dysarthric speech , 2007, Speech Commun..

[23]  Tomoki Toda,et al.  Maximum a posteriori adaptation for many-to-one eigenvoice conversion , 2008, INTERSPEECH.

[24]  Alex Acero,et al.  Robust bandwidth extension of noise-corrupted narrowband speech , 2005, INTERSPEECH.

[25]  Takashi Nose,et al.  A Style Control Technique for HMM-Based Expressive Speech Synthesis , 2007, IEICE Trans. Inf. Syst..

[26]  Tomoki Toda,et al.  Regression approaches to voice quality controll based on one-to-many eigenvoice conversion , 2007, SSW.

[27]  Alan W. Black,et al.  Unit selection in a concatenative speech synthesis system using a large speech database , 1996, 1996 IEEE International Conference on Acoustics, Speech, and Signal Processing Conference Proceedings.

[28]  Tomoki Toda,et al.  Speaker adaptive training for one-to-many eigenvoice conversion based on Gaussian mixture model , 2007, INTERSPEECH.

[29]  Tomoki Toda,et al.  Voice conversion for various types of body transmitted speech , 2009, 2009 IEEE International Conference on Acoustics, Speech and Signal Processing.

[30]  Tomoki Toda,et al.  One-to-Many and Many-to-One Voice Conversion Based on Eigenvoices , 2007, 2007 IEEE International Conference on Acoustics, Speech and Signal Processing - ICASSP '07.

[31]  Yoshinori Sagisaka,et al.  Acoustic characteristics of speaker individuality: Control and conversion , 1995, Speech Commun..

[32]  Hui Ye,et al.  Quality-enhanced voice morphing using maximum likelihood transformations , 2006, IEEE Transactions on Audio, Speech, and Language Processing.

[33]  Satoshi Nakamura,et al.  Voice conversion through vector quantization , 1988, ICASSP-88., International Conference on Acoustics, Speech, and Signal Processing.

[34]  Tomoki Toda,et al.  Evaluation of speaking-aid system with voice conversion for laryngectomees toward its use in practical environments , 2008, INTERSPEECH.

[35]  Athanasios Mouchtaris,et al.  Nonparallel training for voice conversion based on a parameter adaptation approach , 2006, IEEE Transactions on Audio, Speech, and Language Processing.