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Previous works on neural text-to-speech (TTS) have been addressed on limited speed in training and inference time, robustness for difficult synthesis conditions, expressiveness, and controllability.
M. Chu and H. Peng, “Objective measure for estimating mean opinion score of synthesized speech,” Apr. 4 2006, uS Patent 7,024,362
2006
Earlier work this paper cites.
2011
Earlier work this paper cites.
S. Bengio, O. Vinyals, N. Jaitly, and N. Shazeer, “Scheduled sampling for sequence prediction with recurrent neural networks,” in Advances in Neural Information Processing Systems , vol. 28, 2015
2015
Earlier work this paper cites.
M. Ranzato, S. Chopra, M. Auli, and W. Zaremba, “Sequence level training with recurrent neural networks,” in 4th International Conference on Learning Representations, ICLR 2016, San Juan, Puerto Rico, May 2-4, 2016, Conference Track Proceedings , 2016
2016
Earlier work this paper cites.
Y. Ganin, E. Ustinova, H. Ajakan, P. Germain, H. Larochelle, F. Laviolette, M. Marchand, and V. Lempitsky, “Domain-adversarial training of neural networks,” The journal of machine learning research , vol. 17, no. 1, pp. 2096–2030, 2016
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, Q. Le, Y. Agiomyrgiannakis, R. Clark, and R. A. Saurous, “Tacotron: Towards end-to-end speech synthesis,” in Proc. Interspeech 2017 , 2017, pp. 4006–4010
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in Advances in Neural Information Processing Systems , vol. 30, 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan et al. , “Natural tts synthesis by conditioning wavenet on mel spectrogram predictions,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4779–4783
2018
Earlier work this paper cites.
D. Stanton, Y. Wang, and R. Skerry-Ryan, “Predicting expressive speaking style from text in end-to-end speech synthesis,” in 2018 IEEE Spoken Language Technology Workshop (SLT) . IEEE, 2018, pp. 595–602
2018
Earlier work this paper cites.
Y. Wang, D. Stanton, Y. Zhang, R.-S. Ryan, E. Battenberg, J. Shor, Y. Xiao, Y. Jia, F. Ren, and R. A. Saurous, “Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis,” in Proceedings of the 35th International Conference on Machine Learning , ser. Proceedings of Machine Learning Research, vol. 80. PMLR, 10–15 Jul 2018, pp. 5180–5189
2018
Cited alongside, same era.
Y. Wu and K. He, “Group normalization,” in Proceedings of the European conference on computer vision (ECCV) , 2018, pp. 3–19
2018
Cited alongside, same era.
Y. Jia, Y. Zhang, R. Weiss, Q. Wang, J. Shen, F. Ren, z. Chen, P. Nguyen, R. Pang, I. Lopez Moreno, and Y. Wu, “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” in Advances in Neural Information Processing Systems , vol. 31, 2018
2018
Cited alongside, same era.
R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 3617–3621
K. Qian, Y. Zhang, S. Chang, X. Yang, and M. Hasegawa-Johnson, “Autovc: Zero-shot voice style transfer with only autoencoder loss,” in International Conference on Machine Learning . PMLR, 2019, pp. 5210–5219
2019
Later among the works it cites.
W.-N. Hsu, Y. Zhang, R. J. Weiss, Y.-A. Chung, Y. Wang, Y. Wu, and J. Glass, “Disentangling correlated speaker and noise for speech synthesis via data augmentation and adversarial factorization,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5901–5905
2019
Later among the works it cites.
J. Yamagishi, C. Veaux, K. MacDonald et al. , “Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit (version 0.92),” https://datashare.ed.ac.uk/handle/10283/3443 , 2019
2019
Later among the works it cites.
G. Wichern, J. Antognini, M. Flynn, L. R. Zhu, E. McQuinn, D. Crow, E. Manilow, and J. Le Roux, “Wham!: Extending speech separation to noisy environments,” in Proc. Interspeech , Sep. 2019
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2019
Cited alongside, same era.
Z. Hodari, O. Watts, and S. King, “Using generative modelling to produce varied intonation for speech synthesis,” in Proc. 10th ISCA Speech Synthesis Workshop , 2019, pp. 239–244
2019
Cited alongside, same era.
Y. Zhang, S. Pan, L. He, and Z. Ling, “Learning latent representations for style control and transfer in end-to-end speech synthesis,” in ICASSP 2019 - 2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2019, pp. 6945–6949
2019
Cited alongside, same era.
W.-N. Hsu, Y. Zhang, R. Weiss, H. Zen, Y. Wu, Y. Cao, and Y. Wang, “Hierarchical generative modeling for controllable speech synthesis,” in International Conference on Learning Representations , 2019
2019
Cited alongside, same era.
Y. Lee and T. Kim, “Robust and fine-grained prosody control of end-to-end speech synthesis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5911–5915
2019
Cited alongside, same era.
Y. Ren, Y. Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech: Fast, robust and controllable text to speech,” in Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
F. Locatello, S. Bauer, M. Lucic, G. Raetsch, S. Gelly, B. Schölkopf, and O. Bachem, “Challenging common assumptions in the unsupervised learning of disentangled representations,” in international conference on machine learning . PMLR, 2019, pp. 4114–4124
2019
Cited alongside, same era.
2019
Later among the works it cites.
J. Kong, J. Kim, and J. Bae, “HiFi-GAN: Generative adversarial networks for efficient and high fidelity speech synthesis,” in Advances in Neural Information Processing Systems , vol. 33, 2020, pp. 17 022–17 033
2020
Later among the works it cites.
G. Sun, Y. Zhang, R. J. Weiss, Y. Cao, H. Zen, and Y. Wu, “Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6264–6268
2020
Later among the works it cites.
K. Peng, W. Ping, Z. Song, and K. Zhao, “Non-autoregressive neural text-to-speech,” in International Conference on Machine Learning . PMLR, 2020, pp. 7586–7598
2020
Later among the works it cites.
K. Qian, Y. Zhang, S. Chang, M. Hasegawa-Johnson, and D. Cox, “Unsupervised speech decomposition via triple information bottleneck,” in International Conference on Machine Learning . PMLR, 2020, pp. 7836–7846
2020
Later among the works it cites.
R. Valle, J. Li, R. Prenger, and B. Catanzaro, “Mellotron: Multispeaker expressive voice synthesis by conditioning on rhythm, pitch and global style tokens,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6189–6193
2020
Later among the works it cites.
Y. Ren, C. Hu, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech 2: Fast and high-quality end-to-end text to speech,” in International Conference on Learning Representations , 2021
2021
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