Built on

D. Griffin and J. Lim, “Signal estimation from modified short-time fourier transform,”
1984
Earlier work this paper cites.
C. Recommendation, “Pulse code modulation (pcm) of voice frequencies,” in
1988
Earlier work this paper cites.
J. Kominek and A. W. Black, “The cmu arctic speech databases,” in
2004
Earlier work this paper cites.
H. Kawahara, “Straight, exploitation of the other aspect of vocoder: Perceptually isomorphic decomposition of speech sounds,”
2006
Earlier work this paper cites.
H. Banno, H. Hata, M. Morise, T. Takahashi, T. Irino, and H. Kawahara, “Implementation of realtime straight speech manipulation system: Report on its first implementation,”
2007
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M. Morise, F. Yokomori, and K. Ozawa, “World: a vocoder-based high-quality speech synthesis system for real-time applications,”
2016
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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,”
Original
2016
Earlier work this paper cites.
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves
2016
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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,”
Original
2017
Earlier work this paper cites.

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Y. Wang, R. Skerry-Ryan, D. Stanton, Y. Wu, R. J. Weiss, N. Jaitly, Z. Yang, Y. Xiao, Z. Chen, S. Bengio
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K. Ito, “The LJ speech dataset,”
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e. a. Christophe Veaux, Junichi Yamagishi, “CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit,” 2017
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Then

J. Lorenzo-Trueba, T. Drugman, J. Latorre, T. Merritt, B. Putrycz, R. Barra-Chicote, A. Moinet, and V. Aggarwal, “Towards achieving robust universal neural vocoding,”
Original
2018
Later among the works it cites.
Y. Ren, Y. Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech: Fast, robust and controllable text to speech,”
Original
2019
Closest in time.
K. Qian, Y. Zhang, S. Chang, X. Yang, and M. Hasegawa-Johnson, “AutoVC: Zero-shot voice style transfer with only autoencoder loss,” in
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J. Serrà, S. Pascual, and C. Segura, “Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion,”
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J.-M. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” in

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2019

Towards Robust Neural Vocoding for Speech Generation: A Survey

Hsu, Po-chun, Wang, Chun-hsuan, Liu, Andy T. et al.

Understand

Recently, neural vocoders have been widely used in speech synthesis tasks, including text-to-speech and voice conversion.

However, when encountering data distribution mismatch between training and inference, neural vocoders trained on real data often degrade in voice quality for unseen scenarios.
In this paper, we train four common neural vocoders, including WaveNet, WaveRNN, FFTNet, Parallel WaveGAN alternately on five different datasets.
To study the robustness of neural vocoders, we evaluate the models using acoustic features from seen/unseen speakers, seen/unseen languages, a text-to-speech model, and a voice conversion model.

Built on

D. Griffin and J. Lim, “Signal estimation from modified short-time fourier transform,”
1984
Earlier work this paper cites.
C. Recommendation, “Pulse code modulation (pcm) of voice frequencies,” in
1988
Earlier work this paper cites.
J. Kominek and A. W. Black, “The cmu arctic speech databases,” in
2004
Earlier work this paper cites.
H. Kawahara, “Straight, exploitation of the other aspect of vocoder: Perceptually isomorphic decomposition of speech sounds,”
2006
Earlier work this paper cites.
H. Banno, H. Hata, M. Morise, T. Takahashi, T. Irino, and H. Kawahara, “Implementation of realtime straight speech manipulation system: Report on its first implementation,”
2007
Earlier work this paper cites.
M. Morise, F. Yokomori, and K. Ozawa, “World: a vocoder-based high-quality speech synthesis system for real-time applications,”
2016
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,”
Original
2016
Earlier work this paper cites.
A. Van den Oord, N. Kalchbrenner, L. Espeholt, O. Vinyals, A. Graves
2016
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,”
Original
2017
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
Original
2017
Cited alongside, same era.
K. Ito, “The LJ speech dataset,”
2017
Cited alongside, same era.
e. a. Christophe Veaux, Junichi Yamagishi, “CSTR VCTK Corpus: English multi-speaker corpus for CSTR voice cloning toolkit,” 2017
2017
Cited alongside, same era.
J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Skerrv-Ryan
2018
Cited alongside, same era.
J.-c. Chou, C.-c. Yeh, H.-y. Lee, and L.-s. Lee, “Multi-target voice conversion without parallel data by adversarially learning disentangled audio representations,”
Original
2018
Cited alongside, same era.

Then

J. Lorenzo-Trueba, T. Drugman, J. Latorre, T. Merritt, B. Putrycz, R. Barra-Chicote, A. Moinet, and V. Aggarwal, “Towards achieving robust universal neural vocoding,”
Original
2018
Later among the works it cites.
Y. Ren, Y. Ruan, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech: Fast, robust and controllable text to speech,”
Original
2019
Closest in time.
K. Qian, Y. Zhang, S. Chang, X. Yang, and M. Hasegawa-Johnson, “AutoVC: Zero-shot voice style transfer with only autoencoder loss,” in
2019
Closest in time.
J. Serrà, S. Pascual, and C. Segura, “Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion,”
Original
2019
Closest in time.
J.-M. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” in

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. Kameoka, T. Kaneko, K. Tanaka, and N. Hojo, “Stargan-vc: Non-parallel many-to-many voice conversion using star generative adversarial networks,” in

2018

Cited alongside, same era.

Z. Jin, A. Finkelstein, G. J. Mysore, and J. Lu, “Fftnet: A real-time speaker-dependent neural vocoder,” in

2018

Cited alongside, same era.

N. Kalchbrenner, E. Elsen, K. Simonyan, S. Noury, N. Casagrande, E. Lockhart, F. Stimberg, A. v. d. Oord, S. Dieleman, and K. Kavukcuoglu, “Efficient neural audio synthesis,”

Original

2018

Cited alongside, same era.

2019

Closest in time.

R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in

2019

Closest in time.

H. Zen, V. Dang, R. Clark, Y. Zhang, R. J. Weiss, Y. Jia, Z. Chen, and Y. Wu, “Libritts: A corpus derived from librispeech for text-to-speech,”

Original

2019

Closest in time.

R. Yamamoto, E. Song, and J.-M. Kim, “Parallel wavegan: A fast waveform generation model based on generative adversarial networks with multi-resolution spectrogram,” in

2020

Closest in time.