The DOI matches its Crossref record; 1 of the 1 references it cites were found in a registry or an index.
Speech Synthesis Based on Hidden Markov Models
Open accessAbstract
This paper gives a general overview of hidden Markov model (HMM)-based speech synthesis, which has recently been demonstrated to be very effective in synthesizing speech. The main advantage of this approach is its flexibility in changing speaker identities, emotions, and speaking styles. This paper also discusses the relation between the HMM-based approach and the more conventional unit-selection approach that has dominated over the last decades. Finally, advanced techniques for future developments are described.
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Cites
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 · cited 1×
“Speaker-adaptive HMMbased synthesis requires as little as 5–7 min of recorded speech from a target speaker to generate a personalized synthetic voice [24]; hence, the average voice model can be easily transformed into a synthetic voice for any number of speakers [25].”
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Cited by
Deep Learning: Methods and Applications
Deng, L., & Yu, D. (2014) · Foundations and Trends® in Signal Processing · doi:10.1561/2000000039 · Cited by 3,480
Natural TTS Synthesis by Conditioning Wavenet on MEL Spectrogram Predictions
Shen, J., Pang, R., Weiss, R. J., Schuster, M., Jaitly, N., Yang, Z., Chen, Z., Zhang, Y., Wang, Y., Skerrv-Ryan, R., et al. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8461368 · Cited by 2,791
Deep Learning: Methods and Applications
Deng, L. (2014) · now publishers, Inc. eBooks · doi:10.1561/9781601988157 · Cited by 1,759
Neural Speech Synthesis with Transformer Network
Li, N., Liu, S., Liu, Y., Zhao, S., & Liu, M. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33016706 · Cited by 754
Merlin: An Open Source Neural Network Speech Synthesis System
Wu, Z., Watts, O., & King, S. (2016) · 9th ISCA Workshop on Speech Synthesis Workshop (SSW 9) · doi:10.21437/ssw.2016-33 · Cited by 299
Metadata
- Authors
- Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K.
- Venue
- Proceedings of the IEEE
- Published
- 2013-05
- Volume
- 101
- Issue
- 5
- Pages
- 1234-1252
- ISSN
- 0018-9219 (ISSN-L)
- 1558-2256
- OpenAlex
- W2111284386
- Cited by
- 404
- References
- 1
- FWCI
- 44.54
- Type
- journal article
- Language
- en
- Open access
- green
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- Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013). Speech Synthesis Based on Hidden Markov Models. Proceedings of the IEEE. https://doi.org/10.1109/jproc.2013.2251852
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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