Built on

R. Eloff, A. Nortje, B. L. van Niekerk, A. Govender, L. Nortje, A. Pretorius, E. van Biljon, E. andvan der Westhuizen, L. van Staden, and H. Kamper, “Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks,”
Original
1904
Earlier work this paper cites.
A. Liu, P.-C. Hsu, and H.-Y. Lee, “Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion,”
Original
1905
Earlier work this paper cites.
A. Tjandra, B. Sisman, M. Zhang, S. Sakti, H. Li, and S. Nakamura, “VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019,”
Original
1905
Earlier work this paper cites.
S. Feng, T. Lee, and Z. Peng, “Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling,”
Original
1906
Earlier work this paper cites.
S. Sakti, R. Maia, S. Sakai, T. Shimizu, and S. Nakamura, “Development of HMM-based Indonesian speech synthesis,” in
2008
Earlier work this paper cites.
S. Sakti, E. Kelana, H. Riza, S. Sakai, K. Markov, and S. Nakamura, “Development of Indonesian large vocabulary continuous speech recognition system within A-STAR project,” in
2008
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
P. K. Muthukumar and A. W. Black, “Automatic discovery of a phonetic inventory for unwritten languages for statistical speech synthesis,” in
2014
Earlier work this paper cites.
L. Badino, C. Canevari, L. Fadiga, and G. Metta, “An auto-encoder based approach to unsupervised learning of subword units,” in
2014
Earlier work this paper cites.
M. Versteegh, X. Anguera, A. Jansen, and E. Dupoux, “The Zero Resource Speech Challenge 2015: Proposed approaches and results,”
2016
Earlier work this paper cites.
L. Ondel, L. Burget, and J. Cernocký, “Variational inference for acoustic unit discovery,” in
2016
Earlier work this paper cites.

Similar

Z. Wu, O. Watts, and S. King, “Merlin: An open source neural network speech synthesis system,” in
2016
Cited alongside, same era.
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
2016
Cited alongside, same era.
S. Mehri, K. Kumar, I. Gulrajani, R. Kumar, S. Jain, J. Sotelo, A. C. Courville, and Y. Bengio, “SampleRNN: An unconditional end-to-end neural audio generation model,”
Original
2016
Cited alongside, same era.
C. Hsu, H. Hwang, Y. Wu, Y. Tsao, and H. Wang, “Voice conversion from non-parallel corpora using variational auto-encoder,” in
2016
Cited alongside, same era.
E. Dunbar, X. N. Cao, J. Benjumea, J. Karadayi, M. Bernard, L. Besacier, X. Anguera, and E. Dupoux, “The Zero Resource Speech Challenge 2017,” in

Then

O. Scharenborg, L. Besacier, A. W. Black, M. Hasegawa-Johnson, F. Metze, G. Neubig, S. Stüker, P. Godard, M. Müller, L. Ondel, S. Palaskar, P. Arthur, F. Ciannella, M. Du, E. Larsen, D. Merkx, R. Riad, L. Wang, and E. Dupoux, “Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the ”speaking rosetta” JSALT 2017 workshop,” in
2018
Later among the works it cites.
J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Ryan, R. A. Saurous, Y. Agiomyrgiannakis, and Y. Wu, “Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions,” in
2018
Later among the works it cites.
N. Li, S. Liu, Y. Liu, S. Zhao, M. Liu, and M. Zhou, “Close to human quality TTS with transformer,”
Original
2018
Later among the works it cites.
J. Chou, C. Yeh, H. Lee, and L. Lee, “Multi-target voice conversion without parallel data by adversarially learning disentangled audio representations,”
Original

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2019

The Zero Resource Speech Challenge 2019: TTS without T

Dunbar, Ewan, Algayres, Robin, Karadayi, Julien et al.

Understand

We present the Zero Resource Speech Challenge 2019, which proposes to build a speech synthesizer without any text or phonetic labels: hence, TTS without T (text-to-speech without text).

We provide raw audio for a target voice in an unknown language (the Voice dataset), but no alignment, text or labels.
Participants must discover subword units in an unsupervised way (using the Unit Discovery dataset) and align them to the voice recordings in a way that works best for the purpose of synthesizing novel utterances from novel speakers, similar to the target speaker's voice.
We describe the metrics used for evaluation, a baseline system consisting of unsupervised subword unit discovery plus a standard TTS system, and a topline TTS using gold phoneme transcriptions.

Built on

R. Eloff, A. Nortje, B. L. van Niekerk, A. Govender, L. Nortje, A. Pretorius, E. van Biljon, E. andvan der Westhuizen, L. van Staden, and H. Kamper, “Unsupervised acoustic unit discovery for speech synthesis using discrete latent-variable neural networks,”
Original
1904
Earlier work this paper cites.
A. Liu, P.-C. Hsu, and H.-Y. Lee, “Unsupervised End-to-End Learning of Discrete Linguistic Units for Voice Conversion,”
Original
1905
Earlier work this paper cites.
A. Tjandra, B. Sisman, M. Zhang, S. Sakti, H. Li, and S. Nakamura, “VQVAE Unsupervised Unit Discovery and Multi-scale Code2Spec Inverter for Zerospeech Challenge 2019,”
Original
1905
Earlier work this paper cites.
S. Feng, T. Lee, and Z. Peng, “Combining Adversarial Training and Disentangled Speech Representation for Robust Zero-Resource Subword Modeling,”
Original
1906
Earlier work this paper cites.
S. Sakti, R. Maia, S. Sakai, T. Shimizu, and S. Nakamura, “Development of HMM-based Indonesian speech synthesis,” in
2008
Earlier work this paper cites.
S. Sakti, E. Kelana, H. Riza, S. Sakai, K. Markov, and S. Nakamura, “Development of Indonesian large vocabulary continuous speech recognition system within A-STAR project,” in
2008
Earlier work this paper cites.
D. Povey, A. Ghoshal, G. Boulianne, L. Burget, O. Glembek, N. Goel, M. Hannemann, P. Motlicek, Y. Qian, P. Schwarz
2011
Earlier work this paper cites.
P. K. Muthukumar and A. W. Black, “Automatic discovery of a phonetic inventory for unwritten languages for statistical speech synthesis,” in
2014
Earlier work this paper cites.
L. Badino, C. Canevari, L. Fadiga, and G. Metta, “An auto-encoder based approach to unsupervised learning of subword units,” in
2014
Earlier work this paper cites.
M. Versteegh, X. Anguera, A. Jansen, and E. Dupoux, “The Zero Resource Speech Challenge 2015: Proposed approaches and results,”
2016
Earlier work this paper cites.
L. Ondel, L. Burget, and J. Cernocký, “Variational inference for acoustic unit discovery,” in
2016
Earlier work this paper cites.

Similar

Z. Wu, O. Watts, and S. King, “Merlin: An open source neural network speech synthesis system,” in
2016
Cited alongside, same era.
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
2016
Cited alongside, same era.
S. Mehri, K. Kumar, I. Gulrajani, R. Kumar, S. Jain, J. Sotelo, A. C. Courville, and Y. Bengio, “SampleRNN: An unconditional end-to-end neural audio generation model,”
Original
2016
Cited alongside, same era.
C. Hsu, H. Hwang, Y. Wu, Y. Tsao, and H. Wang, “Voice conversion from non-parallel corpora using variational auto-encoder,” in
2016
Cited alongside, same era.
E. Dunbar, X. N. Cao, J. Benjumea, J. Karadayi, M. Bernard, L. Besacier, X. Anguera, and E. Dupoux, “The Zero Resource Speech Challenge 2017,” in

Then

O. Scharenborg, L. Besacier, A. W. Black, M. Hasegawa-Johnson, F. Metze, G. Neubig, S. Stüker, P. Godard, M. Müller, L. Ondel, S. Palaskar, P. Arthur, F. Ciannella, M. Du, E. Larsen, D. Merkx, R. Riad, L. Wang, and E. Dupoux, “Linguistic unit discovery from multi-modal inputs in unwritten languages: Summary of the ”speaking rosetta” JSALT 2017 workshop,” in
2018
Later among the works it cites.
J. Shen, R. Pang, R. J. Weiss, M. Schuster, N. Jaitly, Z. Yang, Z. Chen, Y. Zhang, Y. Wang, R. Ryan, R. A. Saurous, Y. Agiomyrgiannakis, and Y. Wu, “Natural TTS synthesis by conditioning wavenet on MEL spectrogram predictions,” in
2018
Later among the works it cites.
N. Li, S. Liu, Y. Liu, S. Zhao, M. Liu, and M. Zhou, “Close to human quality TTS with transformer,”
Original
2018
Later among the works it cites.
J. Chou, C. Yeh, H. Lee, and L. Lee, “Multi-target voice conversion without parallel data by adversarially learning disentangled audio representations,”
Original

Beyond the bibliography

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

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2017

Cited alongside, same era.

A.-F. Myrman and G. Salvi, “Partitioning of posteriorgrams using siamese models for unsupervised acoustic modelling,” in

2017

Cited alongside, same era.

M. Heck, S. Sakti, and S. Nakamura, “Feature optimized DPGMM clustering for unsupervised subword modeling: A contribution to ZeroSpeech 2017,” in

2017

Cited alongside, same era.

W. Ping, K. Peng, A. Gibiansky, S. Ö. Arik, A. Kannan, S. Narang, J. Raiman, and J. Miller, “Deep voice 3: 2000-speaker neural text-to-speech,”

Original

2017

Cited alongside, same era.

A. Tjandra, S. Sakti, and S. Nakamura, “Listening while speaking: Speech chain by deep learning,” in

2017

Cited alongside, same era.

T. Kaneko and H. Kameoka, “Parallel-data-free voice conversion using cycle-consistent adversarial networks,”

Original

2017

Cited alongside, same era.

A. van den Oord, O. Vinyals

2017

Cited alongside, same era.

2018

Later among the works it cites.

Y. Gao, R. Singh, and B. Raj, “Voice impersonation using generative adversarial networks,” in

2018

Later among the works it cites.

L. Ondel, P. Godard, L. Besacier, E. Larsen, M. Hasegawa-Johnson, O. Scharenborg, E. Dupoux, L. Burget, F. Yvon, and S. Khudanpur, “Bayesian models for unit discovery on a very low resource language,” in

2018

Later among the works it cites.

J. Chorowski, R. J. Weiss, S. Bengio, and A. v. d. Oord, “Unsupervised speech representation learning using wavenet autoencoders,”

Original

2019

Closest in time.

K. Pandia and H. Murthy, “Zero Resource Speech Synthesis Using Transcripts Derived from Perceptual Acoustic Units,”

2019

Closest in time.

S. Nayak, C. S. Kumar, G. Ramesh, S. Bhati, and K. S. R. Murty, “Virtual Phone Discovery for Speech Synthesis,” 2019. [Online]. Available:

2019

Closest in time.

B. Yusuf, A. Gok, B. Gundogdu, O. D. Kose, and M. Saraclar, “Temporally-Aware Acoustic Unit Discovery for Zerospeech 2019 Challenge,”

2019

Closest in time.