Built on

D. Griffin and J. Lim, “Signal estimation from modified short-time fourier transform,” IEEE Transactions on Acoustics, Speech, and Signal Processing , vol. 32, no. 2, pp. 236–243, 1984
1984
Earlier work this paper cites.
Q. Tian, Z. Zhang, H. Lu, L.-H. Chen, and S. Liu, “Featherwave: An efficient high-fidelity neural vocoder with multi-band linear prediction,” arXiv preprint arXiv:2005.05551 , 2020
Original
2011
Earlier work this paper cites.
A. Graves, “Generating sequences with recurrent neural networks,” arXiv preprint arXiv:1308.0850 , 2013
Original
2013
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014
Original
2014
Earlier work this paper cites.
M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025 , 2015
Original
2015
Earlier work this paper cites.
J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” in Advances in neural information processing systems , 2015, pp. 577–585
2015
Earlier work this paper cites.
A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio.” in SSW , 2016, p. 125
2016
Earlier work this paper cites.

Similar

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
Cited alongside, same era.
C. Raffel, M.-T. Luong, P. J. Liu, R. J. Weiss, and D. Eck, “Online and linear-time attention by enforcing monotonic alignments,” arXiv preprint arXiv:1704.00784 , 2017
Original
2017
Cited alongside, same era.
S. Narang, E. Undersander, and G. Diamos, “Block-sparse recurrent neural networks,” arXiv preprint arXiv:1711.02782 , 2017
Original
2017
Cited alongside, same era.
S. Narang, E. Elsen, G. Diamos, and S. Sengupta, “Exploring sparsity in recurrent neural networks,” arXiv preprint arXiv:1704.05119 , 2017
Original
2017
Cited alongside, same era.

Then

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 , 2019, pp. 3171–3180
2019
Later among the works it cites.
M. He, Y. Deng, and L. He, “Robust sequence-to-sequence acoustic modeling with stepwise monotonic attention for neural tts,” arXiv preprint arXiv:1906.00672 , 2019
Original
2019
Later among the works it cites.
K. Kastner, J. F. Santos, Y. Bengio, and A. Courville, “Representation mixing for tts synthesis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5906–5910
2019
Later among the works it cites.
E. Battenberg, S. Mariooryad, D. Stanton, R. Skerry-Ryan, M. Shannon, D. Kao, and T. Bagby, “Effective use of variational embedding capacity in expressive end-to-end speech synthesis,” arXiv preprint arXiv:1906.03402 , 2019
Original
2019
Later among the works it cites.

Beyond the bibliography

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2020

FeatherTTS: Robust and Efficient attention based Neural TTS

Tian, Qiao, Zhang, Zewang, Liu, Chao et al.

Understand

Attention based neural TTS is elegant speech synthesis pipeline and has shown a powerful ability to generate natural speech.

However, it is still not robust enough to meet the stability requirements for industrial products.
Besides, it suffers from slow inference speed owning to the autoregressive generation process.
In this work, we propose FeatherTTS, a robust and efficient attention-based neural TTS system.

Built on

D. Griffin and J. Lim, “Signal estimation from modified short-time fourier transform,” IEEE Transactions on Acoustics, Speech, and Signal Processing , vol. 32, no. 2, pp. 236–243, 1984
1984
Earlier work this paper cites.
Q. Tian, Z. Zhang, H. Lu, L.-H. Chen, and S. Liu, “Featherwave: An efficient high-fidelity neural vocoder with multi-band linear prediction,” arXiv preprint arXiv:2005.05551 , 2020
Original
2011
Earlier work this paper cites.
A. Graves, “Generating sequences with recurrent neural networks,” arXiv preprint arXiv:1308.0850 , 2013
Original
2013
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014
Original
2014
Earlier work this paper cites.
M.-T. Luong, H. Pham, and C. D. Manning, “Effective approaches to attention-based neural machine translation,” arXiv preprint arXiv:1508.04025 , 2015
Original
2015
Earlier work this paper cites.
J. K. Chorowski, D. Bahdanau, D. Serdyuk, K. Cho, and Y. Bengio, “Attention-based models for speech recognition,” in Advances in neural information processing systems , 2015, pp. 577–585
2015
Earlier work this paper cites.
A. Van Den Oord, S. Dieleman, H. Zen, K. Simonyan, O. Vinyals, A. Graves, N. Kalchbrenner, A. W. Senior, and K. Kavukcuoglu, “Wavenet: A generative model for raw audio.” in SSW , 2016, p. 125
2016
Earlier work this paper cites.

Similar

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
Cited alongside, same era.
C. Raffel, M.-T. Luong, P. J. Liu, R. J. Weiss, and D. Eck, “Online and linear-time attention by enforcing monotonic alignments,” arXiv preprint arXiv:1704.00784 , 2017
Original
2017
Cited alongside, same era.
S. Narang, E. Undersander, and G. Diamos, “Block-sparse recurrent neural networks,” arXiv preprint arXiv:1711.02782 , 2017
Original
2017
Cited alongside, same era.
S. Narang, E. Elsen, G. Diamos, and S. Sengupta, “Exploring sparsity in recurrent neural networks,” arXiv preprint arXiv:1704.05119 , 2017
Original
2017
Cited alongside, same era.

Then

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 , 2019, pp. 3171–3180
2019
Later among the works it cites.
M. He, Y. Deng, and L. He, “Robust sequence-to-sequence acoustic modeling with stepwise monotonic attention for neural tts,” arXiv preprint arXiv:1906.00672 , 2019
Original
2019
Later among the works it cites.
K. Kastner, J. F. Santos, Y. Bengio, and A. Courville, “Representation mixing for tts synthesis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5906–5910
2019
Later among the works it cites.
E. Battenberg, S. Mariooryad, D. Stanton, R. Skerry-Ryan, M. Shannon, D. Kao, and T. Bagby, “Effective use of variational embedding capacity in expressive end-to-end speech synthesis,” arXiv preprint arXiv:1906.03402 , 2019
Original
2019
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. 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

Cited alongside, same era.

J.-X. Zhang, Z.-H. Ling, and L.-R. Dai, “Forward attention in sequence-to-sequence acoustic modeling for speech synthesis,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 4789–4793

2018

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, 2019, pp. 6706–6713

2019

Cited alongside, same era.

J.-M. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 5891–5895

2019

Later among the works it cites.

E. Battenberg, R. 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) . IEEE, 2020, pp. 6194–6198

2020

Closest in time.

Z. Zhang, Q. Tian, H. Lu, L.-H. Chen, and S. Liu, “Adadurian: Few-shot adaptation for neural text-to-speech with durian,” arXiv preprint arXiv:2005.05642 , 2020

Original

2020

Closest in time.