The DOI matches its Crossref record; 28 of the 28 references it cites were found in a registry or an index.
Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory
Abstract
In this paper, we describe a novel spectral conversion method for voice conversion (VC). A Gaussian mixture model (GMM) of the joint probability density of source and target features is employed for performing spectral conversion between speakers. The conventional method converts spectral parameters frame by frame based on the minimum mean square error. Although it is reasonably effective, the deterioration of speech quality is caused by some problems: 1) appropriate spectral movements are not always caused by the frame-based conversion process, and 2) the converted spectra are excessively smoothed by statistical modeling. In order to address those problems, we propose a conversion method based on the maximum-likelihood estimation of a spectral parameter trajectory. Not only static but also dynamic feature statistics are used for realizing the appropriate converted spectrum sequence. Moreover, the oversmoothing effect is alleviated by considering a global variance feature of the converted spectra. Experimental results indicate that the performance of VC can be dramatically improved by the proposed method in view of both speech quality and conversion accuracy for speaker individuality.
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Cites
Kawahara, H., Masuda-Katsuse, I., & de Cheveigné, A. (1999) · Speech Communication · doi:10.1016/s0167-6393(98)00085-5 · Cited by 1,831
Speech parameter generation algorithms for HMM-based speech synthesis
Tokuda, K., Yoshimura, T., Masuko, T., Kobayashi, T., & Kitamura, T. (2002) · 2000 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.00CH37100) · doi:10.1109/icassp.2000.861820 · Cited by 999
Self-citationContinuous probabilistic transform for voice conversion
Stylianou, Y., Cappe, O., & Moulines, E. (1998) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.661472 · Cited by 931
A robust parser for spoken language understanding
Wang, Y.-Y. (1999) · 6th European Conference on Speech Communication and Technology · doi:10.21437/eurospeech.1999-513 · Cited by 653
Spectral voice conversion for text-to-speech synthesis
Kain, A., & Macon, M. W. (2002) · Proceedings of the 1998 IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP '98 (Cat. No.98CH36181) · doi:10.1109/icassp.1998.674423 · Cited by 552
A Speech Parameter Generation Algorithm Considering Global Variance for HMM-Based Speech Synthesis
TODA, T., & TOKUDA, K. (2007) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e90-d.5.816 · Cited by 358
Self-citationVoice transformation using PSOLA technique
Valbret, H., Moulines, É., & Tubach, J.-P. (1992) · Speech Communication · doi:10.1016/0167-6393(92)90012-v · Cited by 340
An HMM-based speech synthesis system applied to English
(2002) · Proceedings of 2002 IEEE Workshop on Speech Synthesis 2002 WSS-02 · doi:10.1109/wss.2002.1224415 · Cited by 319
Details of the Nitech HMM-Based Speech Synthesis System for the Blizzard Challenge 2005
ZEN, H., TODA, T., NAKAMURA, M., & TOKUDA, K. (2007) · IEICE Transactions on Information and Systems · doi:10.1093/ietisy/e90-1.1.325 · Cited by 222
Self-citationCMU ARCTIC databases for speech synthesis
Kominek, J. (2003) · Cited by 221
Research works printed but not linked: 18
- 10.1109/ICASSP.1995.479266 · doi:10.1109/icassp.1995.479266
- wrench, 1999, The MOCHA-TIMIT Articulatory Database
- 10.1109/ASRU.2003.1318449 · doi:10.1109/asru.2003.1318449
- toda, 2005, nam-to-speech conversion with gaussian mixture models, Proc INTERSPEECH, 1957
- nakagiri, 2006, improving body transmitted unvoiced speech with statistical voice conversion, Proc INTERSPEECH, 2270
- nakamura, 2006, speaking aid system for total laryngectomees using voice conversion of body transmitted artificial speech, Proc INTERSPEECH, 1395
- 10.1016/0167-6393(94)00053-D · doi:10.1016/0167-6393(94)00053-d
- 10.1250/ast.14.353 · doi:10.1250/ast.14.353
- park, 2000, narrowband to wideband conversion of speech using gmm based transformation, Proc ICASSP, 1847
- yoshimura, 1999, simultaneous modeling of spectrum, pitch and duration in hmm-based speech synthesis, Proc EUROSPEECH, 2347, 10.21437/Eurospeech.1999-596 · doi:10.21437/eurospeech.1999-596
- mashimo, 2002, cross-language voice conversion evaluation using bilingual databases, IPSJ J, 43, 2177
- shiga, 2004, accurate spectral envelope estimation for articulation-to-speech synthesis, Proc 5th ISCA Speech Synth Workshop, 19
- seltzer, 2005, robust bandwidth extension of noise-corrupted narrowband speech, Proc INTERSPEECH, 1509
- toda, 2004, acoustic-to-articulatory inversion mapping with gaussian mixture model, Proc INTERSPEECH, 1129, 10.21437/Interspeech.2004-410 · doi:10.21437/interspeech.2004-410
- 10.1109/ICASSP.1995.479684 · doi:10.1109/icassp.1995.479684
- 10.1109/ICASSP.2005.1415038 · doi:10.1109/icassp.2005.1415038
- 10.1109/ICASSP.2005.1415037 · doi:10.1109/icassp.2005.1415037
- gillett, 2003, transforming contours, Proc INTERSPEECH, 101
Cited by
Spoofing and countermeasures for speaker verification: A survey
Wu, Z., Evans, N., Kinnunen, T., Yamagishi, J., Alegre, F., & Li, H. (2015) · Speech Communication · doi:10.1016/j.specom.2014.10.005 · Cited by 601
Masood, M., Nawaz, M., Malik, K. M., Javed, A., Irtaza, A., & Malik, H. (2023) · Applied Intelligence · doi:10.1007/s10489-022-03766-z · Cited by 543
ASVspoof 2015: the first automatic speaker verification spoofing and countermeasures challenge
Wu, Z., Kinnunen, T., Evans, N., Yamagishi, J., Hanilçi, C., Sahidullah, M., & Sizov, A. (2015) · Interspeech 2015 · doi:10.21437/interspeech.2015-462 · Cited by 484
Data Augmentation for Deep Neural Network Acoustic Modeling
Cui, X., Goel, V., & Kingsbury, B. (2015) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2015.2438544 · Cited by 376
“StarGAN-VC: non-parallel many-to-many voice conversion using star generative adversarial networks,”
Kameoka, H., Kaneko, T., Tanaka, K., & Hojo, N. (2018) · 2018 IEEE Spoken Language Technology Workshop (SLT) · doi:10.1109/slt.2018.8639535 · Cited by 371
Metadata
- Authors
- Toda, T., Black, A. W., & Tokuda, K.
- Venue
- IEEE Transactions on Audio, Speech and Language Processing
- Published
- 2007-11
- Volume
- 15
- Issue
- 8
- Pages
- 2222-2235
- ISSN
- 1558-7916 (ISSN-L)
- OpenAlex
- W2120605154
- Cited by
- 937
- References
- 28
- FWCI
- 28.37
- Type
- journal article
- Language
- en
- Open access
- closed
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- Toda, T., Black, A. W., & Tokuda, K. (2007). Voice Conversion Based on Maximum-Likelihood Estimation of Spectral Parameter Trajectory. IEEE Transactions on Audio, Speech and Language Processing. https://doi.org/10.1109/tasl.2007.907344
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