Built on

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio,” arXiv preprint arXiv:1609.03499 , 2016
Original
2016
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450 , 2016
Original
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 et al. , “Tacotron: Towards end-to-end speech synthesis,” arXiv preprint arXiv:1703.10135 , 2017
Original
2017
Earlier work this paper cites.
W. Ping, K. Peng, A. Gibiansky, S. O. Arik, A. Kannan, S. Narang, J. Raiman, and J. Miller, “Deep voice 3: Scaling text-to-speech with convolutional sequence learning,” arXiv preprint arXiv:1710.07654 , 2017
Original
2017
Earlier work this paper cites.
M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi.” in Interspeech , vol. 2017, 2017, pp. 498–502
2017
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P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for activation functions,” arXiv preprint arXiv:1710.05941 , 2017
Original
2017
Earlier work this paper cites.
K. Ito and L. Johnson, “The lj speech dataset,” Online: https://keithito. com/LJ-Speech-Dataset , 2017
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
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W. Ping, K. Peng, and J. Chen, “Clarinet: Parallel wave generation in end-to-end text-to-speech,” arXiv preprint arXiv:1807.07281 , 2018
Original
2018
Earlier work this paper cites.
S. Elfwing, E. Uchibe, and K. Doya, “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,” Neural Networks , vol. 107, pp. 3–11, 2018
2018
Earlier work this paper cites.

Similar

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
2019
Cited alongside, same era.
N. Li, S. Liu, Y. Liu, S. Zhao, and M. Liu, “Neural speech synthesis with transformer network,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 6706–6713
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,” arXiv preprint arXiv:1905.09263 , 2019
Original
2019
Cited alongside, same era.
Y. Lee, J. Shin, and K. Jung, “Bidirectional variational inference for non-autoregressive text-to-speech,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.

Then

Y. Ren, C. Hu, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech 2: Fast and high-quality end-to-end text-to-speech,” arXiv preprint arXiv:2006.04558 , 2020
Original
2020
Later among the works it cites.
J. Vainer and O. Dušek, “Speedyspeech: Efficient neural speech synthesis,” arXiv preprint arXiv:2008.03802 , 2020
Original
2020
Later among the works it cites.
C. Miao, S. Liang, M. Chen, J. Ma, S. Wang, and J. Xiao, “Flow-tts: A non-autoregressive network for text to speech based on flow,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 7209–7213
2020
Later among the works it cites.
J. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A generative flow for text-to-speech via monotonic alignment search,” arXiv preprint arXiv:2005.11129 , 2020
Original
2020
Later among the works it cites.

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2021

Diff-TTS: A Denoising Diffusion Model for Text-to-Speech

Jeong, Myeonghun, Kim, Hyeongju, Cheon, Sung Jun et al.

Understand

Although neural text-to-speech (TTS) models have attracted a lot of attention and succeeded in generating human-like speech, there is still room for improvements to its naturalness and architectural efficiency.

In this work, we propose a novel non-autoregressive TTS model, namely Diff-TTS, which achieves highly natural and efficient speech synthesis.
Given the text, Diff-TTS exploits a denoising diffusion framework to transform the noise signal into a mel-spectrogram via diffusion time steps.
In order to learn the mel-spectrogram distribution conditioned on the text, we present a likelihood-based optimization method for TTS.

Built on

D. P. Kingma and J. Ba, “Adam: A method for stochastic optimization,” arXiv preprint arXiv:1412.6980 , 2014
Original
2014
Earlier work this paper cites.
A. v. d. Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio,” arXiv preprint arXiv:1609.03499 , 2016
Original
2016
Earlier work this paper cites.
J. L. Ba, J. R. Kiros, and G. E. Hinton, “Layer normalization,” arXiv preprint arXiv:1607.06450 , 2016
Original
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 et al. , “Tacotron: Towards end-to-end speech synthesis,” arXiv preprint arXiv:1703.10135 , 2017
Original
2017
Earlier work this paper cites.
W. Ping, K. Peng, A. Gibiansky, S. O. Arik, A. Kannan, S. Narang, J. Raiman, and J. Miller, “Deep voice 3: Scaling text-to-speech with convolutional sequence learning,” arXiv preprint arXiv:1710.07654 , 2017
Original
2017
Earlier work this paper cites.
M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi.” in Interspeech , vol. 2017, 2017, pp. 498–502
2017
Earlier work this paper cites.
P. Ramachandran, B. Zoph, and Q. V. Le, “Searching for activation functions,” arXiv preprint arXiv:1710.05941 , 2017
Original
2017
Earlier work this paper cites.
K. Ito and L. Johnson, “The lj speech dataset,” Online: https://keithito. com/LJ-Speech-Dataset , 2017
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.
W. Ping, K. Peng, and J. Chen, “Clarinet: Parallel wave generation in end-to-end text-to-speech,” arXiv preprint arXiv:1807.07281 , 2018
Original
2018
Earlier work this paper cites.
S. Elfwing, E. Uchibe, and K. Doya, “Sigmoid-weighted linear units for neural network function approximation in reinforcement learning,” Neural Networks , vol. 107, pp. 3–11, 2018
2018
Earlier work this paper cites.

Similar

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
2019
Cited alongside, same era.
N. Li, S. Liu, Y. Liu, S. Zhao, and M. Liu, “Neural speech synthesis with transformer network,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 6706–6713
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,” arXiv preprint arXiv:1905.09263 , 2019
Original
2019
Cited alongside, same era.
Y. Lee, J. Shin, and K. Jung, “Bidirectional variational inference for non-autoregressive text-to-speech,” in International Conference on Learning Representations , 2020
2020
Cited alongside, same era.

Then

Y. Ren, C. Hu, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech 2: Fast and high-quality end-to-end text-to-speech,” arXiv preprint arXiv:2006.04558 , 2020
Original
2020
Later among the works it cites.
J. Vainer and O. Dušek, “Speedyspeech: Efficient neural speech synthesis,” arXiv preprint arXiv:2008.03802 , 2020
Original
2020
Later among the works it cites.
C. Miao, S. Liang, M. Chen, J. Ma, S. Wang, and J. Xiao, “Flow-tts: A non-autoregressive network for text to speech based on flow,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 7209–7213
2020
Later among the works it cites.
J. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A generative flow for text-to-speech via monotonic alignment search,” arXiv preprint arXiv:2005.11129 , 2020
Original
2020
Later among the works it cites.

Beyond the bibliography

alphaXiv searches the wider corpus for related work and actual follow-ups.

Open on alphaXiv

alphaXiv is searching for related work…

J. Kong, J. Kim, and J. Bae, “Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,” arXiv preprint arXiv:2010.05646 , 2020

Original

2020

Cited alongside, same era.

R. Valle, K. Shih, R. Prenger, and B. Catanzaro, “Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis,” arXiv preprint arXiv:2005.05957 , 2020

Original

2020

Cited alongside, same era.

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

Cited alongside, same era.

A. Łańcucki, “Fastpitch: Parallel text-to-speech with pitch prediction,” arXiv preprint arXiv:2006.06873 , 2020

Original

2020

Cited alongside, same era.

C. Miao, S. Liang, Z. Liu, M. Chen, J. Ma, S. Wang, and J. Xiao, “Efficienttts: An efficient and high-quality text-to-speech architecture,” arXiv preprint arXiv:2012.03500 , 2020

Original

2020

Cited alongside, same era.

J. Donahue, S. Dieleman, M. Bińkowski, E. Elsen, and K. Simonyan, “End-to-end adversarial text-to-speech,” arXiv preprint arXiv:2006.03575 , 2020

Original