Built on

D. B. Paul and J. M. Baker, “The design for the wall street journal-based csr corpus,” in Proceedings of the workshop on Speech and Natural Language . Association for Computational Linguistics, 1992, pp. 357–362
1992
Earlier work this paper cites.
P. Denes and E. Pinson, The Speech Chain , ser. Anchor books. Worth Publishers, 1993. [Online]. Available: https://books.google.co.jp/books?id=ZMTm3nlDfroC
1993
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
X. Zhu and Z. Ghahramani, “Learning from labeled and unlabeled data with label propagation,” Tech. Rep., 2002
2002
Earlier work this paper cites.
T. Toda, A. W. Black, and K. Tokuda, “Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 15, no. 8, pp. 2222–2235, 2007
2007
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, J. Silovsky, G. Stemmer, and K. Vesely, “The Kaldi speech recognition toolkit,” in IEEE 2011 Workshop on Automatic Speech Recognition and Understanding . IEEE Signal Processing Society, Dec. 2011, iEEE Catalog No.: CFP11SRW-USB
2011
Earlier work this paper cites.
N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 4, pp. 788–798, 2011
2011
Earlier work this paper cites.
A. Graves, “Supervised sequence labelling,” in Supervised Sequence Labelling with Recurrent Neural Networks . Springer, 2012, pp. 5–13
2012
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence-to-Sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Earlier work this paper cites.

Similar

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555 , 2014
Original
2014
Cited alongside, same era.
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
Cited alongside, same era.
P. Swietojanski and S. Renals, “Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models,” in Spoken Language Technology Workshop (SLT), 2014 IEEE . IEEE, 2014, pp. 171–176
2014
Cited alongside, same era.
B. Xu, N. Wang, T. Chen, and M. Li, “Empirical evaluation of rectified activations in convolutional network,” arXiv preprint arXiv:1505.00853 , 2015
Original
2015
Cited alongside, same era.

Then

A. 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
Later among the works it cites.
A. Tjandra, S. Sakti, and S. Nakamura, “Listening while speaking: Speech chain by deep learning,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , Dec 2017, pp. 301–308
2017
Later among the works it 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: A fully end-to-end text-to-speech synthesis model,” arXiv preprint arXiv:1703.10135 , 2017
Original
2017
Later among the works it cites.
C. Li, X. Ma, B. Jiang, X. Li, X. Zhang, X. Liu, Y. Cao, A. Kannan, and Z. Zhu, “Deep speaker: an end-to-end neural speaker embedding system,” arXiv preprint arXiv:1705.02304 , 2017
Original
2017
Later among the works it cites.

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2018

Machine Speech Chain with One-shot Speaker Adaptation

Tjandra, Andros, Sakti, Sakriani, Nakamura, Satoshi

Understand

In previous work, we developed a closed-loop speech chain model based on deep learning, in which the architecture enabled the automatic speech recognition (ASR) and text-to-speech synthesis (TTS) components to mutually improve their performance.

This was accomplished by the two parts teaching each other using both labeled and unlabeled data.
This approach could significantly improve model performance within a single-speaker speech dataset, but only a slight increase could be gained in multi-speaker tasks.
Furthermore, the model is still unable to handle unseen speakers.

Built on

D. B. Paul and J. M. Baker, “The design for the wall street journal-based csr corpus,” in Proceedings of the workshop on Speech and Natural Language . Association for Computational Linguistics, 1992, pp. 357–362
1992
Earlier work this paper cites.
P. Denes and E. Pinson, The Speech Chain , ser. Anchor books. Worth Publishers, 1993. [Online]. Available: https://books.google.co.jp/books?id=ZMTm3nlDfroC
1993
Earlier work this paper cites.
S. Hochreiter and J. Schmidhuber, “Long short-term memory,” Neural computation , vol. 9, no. 8, pp. 1735–1780, 1997
1997
Earlier work this paper cites.
X. Zhu and Z. Ghahramani, “Learning from labeled and unlabeled data with label propagation,” Tech. Rep., 2002
2002
Earlier work this paper cites.
T. Toda, A. W. Black, and K. Tokuda, “Voice conversion based on maximum-likelihood estimation of spectral parameter trajectory,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 15, no. 8, pp. 2222–2235, 2007
2007
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, J. Silovsky, G. Stemmer, and K. Vesely, “The Kaldi speech recognition toolkit,” in IEEE 2011 Workshop on Automatic Speech Recognition and Understanding . IEEE Signal Processing Society, Dec. 2011, iEEE Catalog No.: CFP11SRW-USB
2011
Earlier work this paper cites.
N. Dehak, P. J. Kenny, R. Dehak, P. Dumouchel, and P. Ouellet, “Front-end factor analysis for speaker verification,” IEEE Transactions on Audio, Speech, and Language Processing , vol. 19, no. 4, pp. 788–798, 2011
2011
Earlier work this paper cites.
A. Graves, “Supervised sequence labelling,” in Supervised Sequence Labelling with Recurrent Neural Networks . Springer, 2012, pp. 5–13
2012
Earlier work this paper cites.
I. Sutskever, O. Vinyals, and Q. V. Le, “Sequence-to-Sequence learning with neural networks,” in Advances in neural information processing systems , 2014, pp. 3104–3112
2014
Earlier work this paper cites.

Similar

J. Chung, C. Gulcehre, K. Cho, and Y. Bengio, “Empirical evaluation of gated recurrent neural networks on sequence modeling,” arXiv preprint arXiv:1412.3555 , 2014
Original
2014
Cited alongside, same era.
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
Cited alongside, same era.
P. Swietojanski and S. Renals, “Learning hidden unit contributions for unsupervised speaker adaptation of neural network acoustic models,” in Spoken Language Technology Workshop (SLT), 2014 IEEE . IEEE, 2014, pp. 171–176
2014
Cited alongside, same era.
B. Xu, N. Wang, T. Chen, and M. Li, “Empirical evaluation of rectified activations in convolutional network,” arXiv preprint arXiv:1505.00853 , 2015
Original
2015
Cited alongside, same era.

Then

A. 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
Later among the works it cites.
A. Tjandra, S. Sakti, and S. Nakamura, “Listening while speaking: Speech chain by deep learning,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , Dec 2017, pp. 301–308
2017
Later among the works it 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: A fully end-to-end text-to-speech synthesis model,” arXiv preprint arXiv:1703.10135 , 2017
Original
2017
Later among the works it cites.
C. Li, X. Ma, B. Jiang, X. Li, X. Zhang, X. Liu, Y. Cao, A. Kannan, and Z. Zhu, “Deep speaker: an end-to-end neural speaker embedding system,” arXiv preprint arXiv:1705.02304 , 2017
Original
2017
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…

Z. Wu, P. Swietojanski, C. Veaux, S. Renals, and S. King, “A study of speaker adaptation for dnn-based speech synthesis,” in Sixteenth Annual Conference of the International Speech Communication Association , 2015

2015

Cited alongside, same era.

D. Bahdanau, J. Chorowski, D. Serdyuk, P. Brakel, and Y. Bengio, “End-to-end attention-based large vocabulary speech recognition,” in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on . IEEE, 2016, pp. 4945–4949

2016

Cited alongside, same era.

W. Chan, N. Jaitly, Q. Le, and O. Vinyals, “Listen, attend and spell: A neural network for large vocabulary conversational speech recognition,” in Acoustics, Speech and Signal Processing (ICASSP), 2016 IEEE International Conference on . IEEE, 2016, pp. 4960–4964

2016

Cited alongside, same era.

A. B. L. Larsen, S. K. Sønderby, H. Larochelle, and O. Winther, “Autoencoding beyond pixels using a learned similarity metric,” in International Conference on Machine Learning , 2016, pp. 1558–1566

2016

Cited alongside, same era.

J. Johnson, A. Alahi, and L. Fei-Fei, “Perceptual losses for real-time style transfer and super-resolution,” in European Conference on Computer Vision . Springer, 2016, pp. 694–711

2016

Cited alongside, same era.

B. McFee, M. McVicar, O. Nieto, S. Balke, C. Thome, D. Liang, E. Battenberg, J. Moore, R. Bittner, R. Yamamoto, and et al., “librosa 0.5.0,” Feb 2017

2017

Later among the works it cites.

A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” 2017

2017

Later among the works it cites.

S. Kim, T. Hori, and S. Watanabe, “Joint ctc-attention based end-to-end speech recognition using multi-task learning,” in Acoustics, Speech and Signal Processing (ICASSP), 2017 IEEE International Conference on . IEEE, 2017, pp. 4835–4839

2017

Later among the works it cites.

A. Tjandra, S. Sakti, and S. Nakamura, “Attention-based wav2text with feature transfer learning,” in 2017 IEEE Automatic Speech Recognition and Understanding Workshop (ASRU) , Dec 2017, pp. 309–315

2017

Later among the works it cites.