The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.
Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis
Open accessAbstract
This paper describes a speaker-adaptive HMM-based speech synthesis system. The new system, called ldquoHTS-2007,rdquo employs speaker adaptation (CSMAPLR+MAP), feature-space adaptive training, mixed-gender modeling, and full-covariance modeling using CSMAPLR transforms, in addition to several other techniques that have proved effective in our previous systems. Subjective evaluation results show that the new system generates significantly better quality synthetic speech than speaker-dependent approaches with realistic amounts of speech data, and that it bears comparison with speaker-dependent approaches even when large amounts of speech data are available. In addition, a comparison study with several speech synthesis techniques shows the new system is very robust: It is able to build voices from less-than-ideal speech data and synthesize good-quality speech even for out-of-domain sentences.
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Cites
An efficient forward-backward algorithm for an explicit-duration hidden Markov model
Yu, S.-Z., & Kobayashi, H. (2003) · IEEE Signal Processing Letters · doi:10.1109/lsp.2002.806705 · Cited by 226
Explicit modelling of state occupancy in hidden Markov models for automatic speech recognition
Russell, M., & Moore, R. (1985) · ICASSP '85. IEEE International Conference on Acoustics, Speech, and Signal Processing · doi:10.1109/icassp.1985.1168477 · Cited by 194
On the complexity of explicit duration HMM's
Mitchell, C., Harper, M., & Jamieson, L. (1995) · IEEE Transactions on Speech and Audio Processing · doi:10.1109/89.388149 · Cited by 63
Cited by
Visual to Sound: Generating Natural Sound for Videos in the Wild
Zhou, Y., Wang, Z., Fang, C., Bui, T., & Berg, T. L. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00374 · Cited by 201
Deep voice 3: scaling text-to-speech with convolutional sequence learning
Ping, W., Peng, K., Gibiansky, A., Arik, S. O., Kannan, A., Narang, S., Raiman, J., & Miller, J. (2017) · arXiv · doi:10.48550/arxiv.1710.07654 · Cited by 313
Yamagishi, J., Usabaev, B., King, S., Watts, O., Dines, J., Tian, J., Guan, Y., Hu, R., Oura, K., Wu, Y.-J., et al. (2010) · IEEE Transactions on Audio, Speech, and Language Processing · doi:10.1109/tasl.2010.2045237 · Cited by 78
Self-citation8. Spoken Dialogue Interfaces for Older People
Ravichander, V., Maria, W., & Steve, R. (2012) · Assistive Technology Research Series · doi:10.3233/978-1-61499-127-4-118 · Cited by 15
Metadata
- Authors
- Yamagishi, J., Nose, T., Zen, H., Ling, Z.-H., Toda, T., Tokuda, K., King, S., & Renals, S.
- Venue
- IEEE Transactions on Audio, Speech, and Language Processing
- Published
- 2009-08
- Volume
- 17
- Issue
- 6
- Pages
- 1208-1230
- ISSN
- 1558-7916 (ISSN-L)
- OpenAlex
- W2117418893
- Cited by
- 178
- References
- 3
- FWCI
- 20.24
- Type
- journal article
- Language
- en
- Open access
- green
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- Yamagishi, J., Nose, T., Zen, H., Ling, Z.-H., Toda, T., Tokuda, K., King, S., & Renals, S. (2009). Robust Speaker-Adaptive HMM-Based Text-to-Speech Synthesis. IEEE Transactions on Audio, Speech, and Language Processing. https://doi.org/10.1109/tasl.2009.2016394
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