Built on

H. Zen, R. Clark, R. J. Weiss, V. Dang, Y. Jia, Y. Wu, Y. Zhang, and Z. Chen, “Libritts: A corpus derived from librispeech for text-to-speech,” in Interspeech , 2019. [Online]. Available: https://arxiv.org/abs/1904.02882
Original
1904
Earlier work this paper cites.
L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE , vol. 77, no. 2, pp. 257–286, Feb. 1989
1989
Earlier work this paper cites.
R. Kubichek, “Mel-cepstral distance measure for objective speech quality assessment,” in Proceedings of IEEE Pacific Rim Conference on Communications Computers and Signal Processing , vol. 1, 1993, pp. 125–128 vol.1
1993
Earlier work this paper cites.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” ser. ICML ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 369–376
2006
Earlier work this paper cites.
A. Graves, “Generating sequences with recurrent neural networks,” CoRR , vol. abs/1308.0850, 2013. [Online]. Available: http://arxiv.org/abs/1308.0850
Original
2013
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” 2014, cite arxiv:1409.0473Comment: Accepted at ICLR 2015 as oral presentation. [Online]. Available: http://arxiv.org/abs/1409.0473
Original
2015
Earlier work this paper cites.
J. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” CoRR , vol. abs/1506.07503, 2015. [Online]. Available: http://arxiv.org/abs/1506.07503
Original
2015
Earlier work this paper cites.
Y. Wang, R. J. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, Q. V. Le, Y. Agiomyrgiannakis, R. Clark, and R. A. Saurous, “Tacotron: A fully end-to-end text-to-speech synthesis model,” CoRR , vol. abs/1703.10135, 2017. [Online]. Available: http://arxiv.org/abs/1703.10135
Original
2017
Earlier work this paper cites.

Similar

J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. J. Skerry-Ryan, R. A. Saurous, Y. Agiomyrgiannakis, and Y. Wu, “Natural TTS synthesis by conditioning wavenet on mel spectrogram predictions,” CoRR , vol. abs/1712.05884, 2017. [Online]. Available: http://arxiv.org/abs/1712.05884
Original
2017
Cited alongside, same era.
M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi,” in INTERSPEECH , 2017
2017
Cited alongside, same era.
H. Tachibana, K. Uenoyama, and S. Aihara, “Efficiently trainable text-to-speech system based on deep convolutional networks with guided attention,” CoRR , vol. abs/1710.08969, 2017. [Online]. Available: http://arxiv.org/abs/1710.08969
Original
2017
Cited alongside, same era.
K. Ito and L. Johnson, “The lj speech dataset,” https://keithito.com/LJ-Speech-Dataset/ , 2017
2017
Cited alongside, same era.

Then

R. Valle, K. Shih, R. Prenger, and B. Catanzaro, “Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis,” 2020
2020
Later among the works it cites.
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. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A generative flow for text-to-speech via monotonic alignment search,” 2020
2020
Later among the works it cites.
A. Łańcucki, “Fastpitch: Parallel text-to-speech with pitch prediction,” 2020
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

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Reading the bibliography…

2021

One TTS Alignment To Rule Them All

Badlani, Rohan, Łancucki, Adrian, Shih, Kevin J. et al.

Understand

Speech-to-text alignment is a critical component of neural textto-speech (TTS) models.

Autoregressive TTS models typically use an attention mechanism to learn these alignments on-line.
However, these alignments tend to be brittle and often fail to generalize to long utterances and out-of-domain text, leading to missing or repeating words.
Most non-autoregressive endto-end TTS models rely on durations extracted from external sources.

Built on

H. Zen, R. Clark, R. J. Weiss, V. Dang, Y. Jia, Y. Wu, Y. Zhang, and Z. Chen, “Libritts: A corpus derived from librispeech for text-to-speech,” in Interspeech , 2019. [Online]. Available: https://arxiv.org/abs/1904.02882
Original
1904
Earlier work this paper cites.
L. R. Rabiner, “A tutorial on hidden Markov models and selected applications in speech recognition,” Proceedings of the IEEE , vol. 77, no. 2, pp. 257–286, Feb. 1989
1989
Earlier work this paper cites.
R. Kubichek, “Mel-cepstral distance measure for objective speech quality assessment,” in Proceedings of IEEE Pacific Rim Conference on Communications Computers and Signal Processing , vol. 1, 1993, pp. 125–128 vol.1
1993
Earlier work this paper cites.
A. Graves, S. Fernández, F. Gomez, and J. Schmidhuber, “Connectionist temporal classification: Labelling unsegmented sequence data with recurrent neural networks,” ser. ICML ’06. New York, NY, USA: Association for Computing Machinery, 2006, p. 369–376
2006
Earlier work this paper cites.
A. Graves, “Generating sequences with recurrent neural networks,” CoRR , vol. abs/1308.0850, 2013. [Online]. Available: http://arxiv.org/abs/1308.0850
Original
2013
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” 2014, cite arxiv:1409.0473Comment: Accepted at ICLR 2015 as oral presentation. [Online]. Available: http://arxiv.org/abs/1409.0473
Original
2015
Earlier work this paper cites.
J. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” CoRR , vol. abs/1506.07503, 2015. [Online]. Available: http://arxiv.org/abs/1506.07503
Original
2015
Earlier work this paper cites.
Y. Wang, R. J. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio, Q. V. Le, Y. Agiomyrgiannakis, R. Clark, and R. A. Saurous, “Tacotron: A fully end-to-end text-to-speech synthesis model,” CoRR , vol. abs/1703.10135, 2017. [Online]. Available: http://arxiv.org/abs/1703.10135
Original
2017
Earlier work this paper cites.

Similar

J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. J. Skerry-Ryan, R. A. Saurous, Y. Agiomyrgiannakis, and Y. Wu, “Natural TTS synthesis by conditioning wavenet on mel spectrogram predictions,” CoRR , vol. abs/1712.05884, 2017. [Online]. Available: http://arxiv.org/abs/1712.05884
Original
2017
Cited alongside, same era.
M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi,” in INTERSPEECH , 2017
2017
Cited alongside, same era.
H. Tachibana, K. Uenoyama, and S. Aihara, “Efficiently trainable text-to-speech system based on deep convolutional networks with guided attention,” CoRR , vol. abs/1710.08969, 2017. [Online]. Available: http://arxiv.org/abs/1710.08969
Original
2017
Cited alongside, same era.
K. Ito and L. Johnson, “The lj speech dataset,” https://keithito.com/LJ-Speech-Dataset/ , 2017
2017
Cited alongside, same era.

Then

R. Valle, K. Shih, R. Prenger, and B. Catanzaro, “Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis,” 2020
2020
Later among the works it cites.
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. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A generative flow for text-to-speech via monotonic alignment search,” 2020
2020
Later among the works it cites.
A. Łańcucki, “Fastpitch: Parallel text-to-speech with pitch prediction,” 2020
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

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

H. Tachibana, K. Uenoyama, and S. Aihara, “Efficiently trainable text-to-speech system based on deep convolutional networks with guided attention,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4784–4788

2018

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 , H. Wallach, H. Larochelle, A. Beygelzimer, F. d'Alché-Buc, E. Fox, and R. Garnett, Eds., vol. 32. Curran Associates, Inc., 2019, pp. 3171–3180

2019

Cited alongside, same era.

J. Li, V. Lavrukhin, B. Ginsburg, R. Leary, O. Kuchaiev, J. M. Cohen, H. Nguyen, and R. T. Gadde, “Jasper: An end-to-end convolutional neural acoustic model,” 2019

Original

2019

Cited alongside, same era.

Later among the works it cites.

E. Battenberg, R. J. Skerry-Ryan, S. Mariooryad, D. Stanton, D. Kao, M. Shannon, and T. Bagby, “Location-relative attention mechanisms for robust long-form speech synthesis,” in ICASSP 2020 - 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) , 2020, pp. 6194–6198

2020

Later among the works it cites.

K. J. Shih, R. Valle, R. Badlani, A. Lancucki, W. Ping, and B. Catanzaro, “RAD-TTS: Parallel flow-based TTS with robust alignment learning and diverse synthesis,” in ICML Workshop on Invertible Neural Networks, Normalizing Flows, and Explicit Likelihood Models , 2021. [Online]. Available: https://openreview.net/forum?id=0NQwnnwAORi

2021

Closest in time.