Fetching the paper…
Reading the bibliography…
Adaptive text to speech (TTS) can synthesize new voices in zero-shot scenarios efficiently, by using a well-trained source TTS model without adapting it on the speech data of new speakers.
J. MacQueen et al. , “Some methods for classification and analysis of multivariate observations,” in Proceedings of the fifth Berkeley symposium on mathematical statistics and probability , vol. 1, no. 14. Oakland, CA, USA, 1967, pp. 281–297
1967
Earlier work this paper cites.
V. Panayotov, G. Chen, D. Povey, and S. Khudanpur, “Librispeech: an asr corpus based on public domain audio books,” in 2015 IEEE international conference on acoustics, speech and signal processing (ICASSP) . IEEE, 2015, pp. 5206–5210
2015
Earlier work this paper cites.
S. Ioffe and C. Szegedy, “Batch normalization: Accelerating deep network training by reducing internal covariate shift,” in International conference on machine learning . PMLR, 2015, pp. 448–456
2015
Earlier work this paper cites.
C. Veaux, J. Yamagishi, and K. MacDonald, “Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit (version 0.92),” 2016. [Online]. Available: https://doi.org/10.7488/ds/1495
2016
Earlier work this paper cites.
A. Gibiansky, S. Arik, G. Diamos, J. Miller, K. Peng, W. Ping, J. Raiman, and Y. Zhou, “Deep voice 2: Multi-speaker neural text-to-speech,” Advances in neural information processing systems , vol. 30, 2017
2017
Earlier work this paper cites.
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.” 2017
2017
Earlier work this paper cites.
A. Vaswani, N. Shazeer, N. Parmar, J. Uszkoreit, L. Jones, A. N. Gomez, L. Kaiser, and I. Polosukhin, “Attention is all you need,” in NIPS , 2017, pp. 5998–6008
2017
Earlier work this paper cites.
K. Ito., “The lj speech dataset,” 2017. [Online]. Available: https://keithito.com/LJ-Speech-Dataset/
2017
Earlier work this paper cites.
R. Skerry-Ryan, E. Battenberg, Y. Xiao, Y. Wang, D. Stanton, J. Shor, R. Weiss, R. Clark, and R. A. Saurous, “Towards end-to-end prosody transfer for expressive speech synthesis with tacotron,” in international conference on machine learning . PMLR, 2018, pp. 4693–4702
2018
Earlier work this paper cites.
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
Earlier work this paper cites.
Y. Wang, D. Stanton, Y. Zhang, R.-S. Ryan, E. Battenberg, J. Shor, Y. Xiao, Y. Jia, F. Ren, and R. A. Saurous, “Style tokens: Unsupervised style modeling, control and transfer in end-to-end speech synthesis,” in International Conference on Machine Learning . PMLR, 2018, pp. 5180–5189
2018
Earlier work this paper 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,” Advances in Neural Information Processing Systems , vol. 32, 2019
2019
Cited alongside, same era.
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,” in INTERSPEECH . ISCA, 2019, pp. 1526–1530
2019
Cited alongside, same era.
M. Chen, X. Tan, Y. Ren, J. Xu, H. Sun, S. Zhao, and T. Qin, “Multispeech: Multi-speaker text to speech with transformer,” in INTERSPEECH . ISCA, 2020, pp. 4024–4028
2020
Cited alongside, same era.
E. Cooper, C.-I. Lai, Y. Yasuda, F. Fang, X. Wang, N. Chen, and J. Yamagishi, “Zero-shot multi-speaker text-to-speech with state-of-the-art neural speaker embeddings,” in ICASSP 2020-2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2020, pp. 6184–6188
2020
Cited alongside, same era.
Y. Yan, X. Tan, B. Li, T. Qin, S. Zhao, Y. Shen, and T. Liu, “Adaspeech 2: Adaptive text to speech with untranscribed data,” in ICASSP . IEEE, 2021, pp. 6613–6617
2021
Later among the works it cites.
2021
Later among the works it cites.
2021
Later among the works it cites.
D. Min, D. B. Lee, E. Yang, and S. J. Hwang, “Meta-stylespeech: Multi-speaker adaptive text-to-speech generation,” in International Conference on Machine Learning . PMLR, 2021, pp. 7748–7759
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
T. Wang, J. Tao, R. Fu, J. Yi, Z. Wen, and C. Qiang, “Bi-level speaker supervision for one-shot speech synthesis.” in INTERSPEECH , 2020, pp. 3989–3993
2020
Cited alongside, same era.
J. Kong, J. Kim, and J. Bae, “Hifi-gan: Generative adversarial networks for efficient and high fidelity speech synthesis,” Advances in Neural Information Processing Systems , vol. 33, pp. 17 022–17 033, 2020
2020
Cited alongside, same era.
2021
Cited alongside, same era.
Y. Ren, C. Hu, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T. Liu, “Fastspeech 2: Fast and high-quality end-to-end text to speech,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 , 2021
2021
Cited alongside, same era.
2021
Cited alongside, same era.
M. Chen, X. Tan, B. Li, Y. Liu, T. Qin, S. Zhao, and T. Liu, “Adaspeech: Adaptive text to speech for custom voice,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 , 2021
2021
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
C.-M. Chien, J.-H. Lin, C.-y. Huang, P.-c. Hsu, and H.-y. Lee, “Investigating on incorporating pretrained and learnable speaker representations for multi-speaker multi-style text-to-speech,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 8588–8592
2021
Later among the works it cites.
D. Xin, T. Komatsu, S. Takamichi, and H. Saruwatari, “Disentangled speaker and language representations using mutual information minimization and domain adaptation for cross-lingual tts,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 6608–6612
2021
Later among the works it cites.
Y. Zheng, X. Li, and L. Lu, “Investigation of fast and efficient methods for multi-speaker modeling and speaker adaptation,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 6618–6622
2021
Later among the works it cites.
D. Xin, Y. Saito, S. Takamichi, T. Koriyama, and H. Saruwatari, “Cross-Lingual Speaker Adaptation Using Domain Adaptation and Speaker Consistency Loss for Text-To-Speech Synthesis,” in Proc. Interspeech 2021 , 2021, pp. 1614–1618
2021
Later among the works it cites.