The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Merlin: An Open Source Neural Network Speech Synthesis System
Open accessAbstract
We introduce the Merlin speech synthesis toolkit for neural network-based speech synthesis. The system takes linguistic features as input, and employs neural networks to predict acoustic features, which are then passed to a vocoder to produce the speech waveform. Various neural netw are implemented, including a standard feedforward neural network, mixture density neural network, recurrent neural network (RNN), long short-term memory (LSTM) recurrent neural network, amongst others. The toolkit is Open Source, written in Python, and is extensible. This paper briefly describes the system, and provides some benchmarking results on a freely available corpus.
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Cites
Empirical Evaluation of Gated Recurrent Neural Networks on Sequence Modeling
Chung, J., Gulcehre, C., Cho, K., & Bengio, Y. (2014) · arXiv · doi:10.48550/arxiv.1412.3555 · Cited by 11,008
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Statistical parametric speech synthesis
Zen, H., Tokuda, K., & Black, A. W. (2009) · Speech Communication · doi:10.1016/j.specom.2009.04.004 · Cited by 1,195
WORLD: A Vocoder-Based High-Quality Speech Synthesis System for Real-Time Applications
MORISE, M., YOKOMORI, F., & OZAWA, K. (2016) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2015edp7457 · Cited by 1,092
Statistical parametric speech synthesis using deep neural networks
Zen, H., Senior, A., & Schuster, M. (2013) · 2013 IEEE International Conference on Acoustics, Speech and Signal Processing · doi:10.1109/icassp.2013.6639215 · Cited by 809
TTS synthesis with bidirectional LSTM based recurrent neural networks
Fan, Y., Qian, Y., Xie, F.-L., & Soong, F. K. (2014) · Interspeech 2014 · doi:10.21437/interspeech.2014-443 · Cited by 426
Speech Synthesis Based on Hidden Markov Models
Tokuda, K., Nankaku, Y., Toda, T., Zen, H., Yamagishi, J., & Oura, K. (2013) · Proceedings of the IEEE · doi:10.1109/jproc.2013.2251852 · Cited by 404
Zen, H., & Sak, H. (2015) · 2015 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2015.7178816 · Cited by 262
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Cited by
ASVspoof 2019: A large-scale public database of synthesized, converted and replayed speech
Wang, X., Yamagishi, J., Todisco, M., Delgado, H., Nautsch, A., Evans, N., Sahidullah, M., Vestman, V., Kinnunen, T., Lee, K. A., et al. (2020) · Computer Speech & Language · doi:10.1016/j.csl.2020.101114 · Cited by 505
(2017) · International Conference on Learning Representations · Cited by 314
The Voice Conversion Challenge 2018: Promoting Development of Parallel and Nonparallel Methods
Lorenzo-Trueba, J., Yamagishi, J., Toda, T., Saito, D., Villavicencio, F., Kinnunen, T., & Ling, Z. (2018) · The Speaker and Language Recognition Workshop (Odyssey 2018) · doi:10.21437/odyssey.2018-28 · Cited by 305
Deep Residual Neural Networks for Audio Spoofing Detection
Alzantot, M., Wang, Z., & Srivastava, M. B. (2019) · Interspeech 2019 · doi:10.21437/interspeech.2019-3174 · Cited by 215
“High-quality nonparallel voice conversion based on cycle-consistent adversarial network,”
Fang, F., Yamagishi, J., Echizen, I., & Lorenzo-Trueba, J. (2018) · 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) · doi:10.1109/icassp.2018.8462342 · Cited by 128
Metadata
- Authors
- Wu, Z., Watts, O., & King, S.
- Venue
- 9th ISCA Workshop on Speech Synthesis Workshop (SSW 9)
- Publisher
- ISCA
- Published
- 2016-09-13
- Pages
- 202-207
- OpenAlex
- W2598638573
- Cited by
- 299
- References
- 24
- FWCI
- 26.86
- Type
- conference paper
- Language
- en
- Open access
- green
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