Built on

E. Variani, X. Lei, E. McDermott, I. L. Moreno, and J. Gonzalez-Dominguez, “Deep neural networks for small footprint text-dependent speaker verification,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2014, pp. 4052–4056
2014
Earlier work this paper cites.
T. Toda, L.-H. Chen, D. Saito, F. Villavicencio, M. Wester, Z. Wu, and J. Yamagishi, “The voice conversion challenge 2016.” in Interspeech , 2016, pp. 1632–1636
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,” arXiv preprint arXiv:1710.07654 , 2017
Original
2017
Earlier work this paper cites.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” in Interspeech , 2017
2017
Earlier work this paper 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: Towards end-to-end speech synthesis,” arXiv preprint arXiv:1703.10135 , 2017
Original
2017
Earlier work this paper cites.
M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi.” in Interspeech , vol. 2017, 2017, pp. 498–502
2017
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.
D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust dnn embeddings for speaker recognition,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5329–5333
2018
Earlier work this paper cites.

Similar

Y. Jia, Y. Zhang, R. Weiss, Q. Wang, J. Shen, F. Ren, P. Nguyen, R. Pang, I. Lopez Moreno, Y. Wu et al. , “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” Advances in neural information processing systems , vol. 31, 2018
2018
Cited alongside, same era.
J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in Interspeech , 2018
2018
Cited alongside, same era.
J. Lorenzo-Trueba, J. Yamagishi, T. Toda, D. Saito, F. Villavicencio, T. Kinnunen, and Z. Ling, “The voice conversion challenge 2018: Promoting development of parallel and nonparallel methods,” arXiv preprint arXiv:1804.04262 , 2018
Original
2018
Cited alongside, same era.
N. S. Ibrahim and D. A. Ramli, “I-vector extraction for speaker recognition based on dimensionality reduction,” Procedia Computer Science , vol. 126, pp. 1534–1540, 2018
2018
Cited alongside, same era.

Then

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,” arXiv preprint arXiv:1904.02882 , 2019
Original
2019
Later among the works it cites.
Y. Ren, C. Hu, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech 2: Fast and high-quality end-to-end text to speech,” arXiv preprint arXiv:2006.04558 , 2020
Original
2020
Later among the works it cites.
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
Later among the works it cites.
B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,” arXiv preprint arXiv:2005.07143 , 2020
Original
2020

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2022

ECAPA-TDNN for Multi-speaker Text-to-speech Synthesis

Xue, Jinlong, Deng, Yayue, Han, Yichen et al.

Understand

In recent years, neural network based methods for multi-speaker text-to-speech synthesis (TTS) have made significant progress.

However, the current speaker encoder models used in these methods still cannot capture enough speaker information.
In this paper, we focus on accurate speaker encoder modeling and propose an end-to-end method that can generate high-quality speech and better similarity for both seen and unseen speakers.
The proposed architecture consists of three separately trained components: a speaker encoder based on the state-of-the-art ECAPA-TDNN model which is derived from speaker verification task, a FastSpeech2 based synthesizer, and a HiFi-GAN vocoder.

Built on

E. Variani, X. Lei, E. McDermott, I. L. Moreno, and J. Gonzalez-Dominguez, “Deep neural networks for small footprint text-dependent speaker verification,” in 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2014, pp. 4052–4056
2014
Earlier work this paper cites.
T. Toda, L.-H. Chen, D. Saito, F. Villavicencio, M. Wester, Z. Wu, and J. Yamagishi, “The voice conversion challenge 2016.” in Interspeech , 2016, pp. 1632–1636
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,” arXiv preprint arXiv:1710.07654 , 2017
Original
2017
Earlier work this paper cites.
A. Nagrani, J. S. Chung, and A. Zisserman, “Voxceleb: a large-scale speaker identification dataset,” in Interspeech , 2017
2017
Earlier work this paper 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: Towards end-to-end speech synthesis,” arXiv preprint arXiv:1703.10135 , 2017
Original
2017
Earlier work this paper cites.
M. McAuliffe, M. Socolof, S. Mihuc, M. Wagner, and M. Sonderegger, “Montreal forced aligner: Trainable text-speech alignment using kaldi.” in Interspeech , vol. 2017, 2017, pp. 498–502
2017
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.
D. Snyder, D. Garcia-Romero, G. Sell, D. Povey, and S. Khudanpur, “X-vectors: Robust dnn embeddings for speaker recognition,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2018, pp. 5329–5333
2018
Earlier work this paper cites.

Similar

Y. Jia, Y. Zhang, R. Weiss, Q. Wang, J. Shen, F. Ren, P. Nguyen, R. Pang, I. Lopez Moreno, Y. Wu et al. , “Transfer learning from speaker verification to multispeaker text-to-speech synthesis,” Advances in neural information processing systems , vol. 31, 2018
2018
Cited alongside, same era.
J. S. Chung, A. Nagrani, and A. Zisserman, “Voxceleb2: Deep speaker recognition,” in Interspeech , 2018
2018
Cited alongside, same era.
J. Lorenzo-Trueba, J. Yamagishi, T. Toda, D. Saito, F. Villavicencio, T. Kinnunen, and Z. Ling, “The voice conversion challenge 2018: Promoting development of parallel and nonparallel methods,” arXiv preprint arXiv:1804.04262 , 2018
Original
2018
Cited alongside, same era.
N. S. Ibrahim and D. A. Ramli, “I-vector extraction for speaker recognition based on dimensionality reduction,” Procedia Computer Science , vol. 126, pp. 1534–1540, 2018
2018
Cited alongside, same era.

Then

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,” arXiv preprint arXiv:1904.02882 , 2019
Original
2019
Later among the works it cites.
Y. Ren, C. Hu, X. Tan, T. Qin, S. Zhao, Z. Zhao, and T.-Y. Liu, “Fastspeech 2: Fast and high-quality end-to-end text to speech,” arXiv preprint arXiv:2006.04558 , 2020
Original
2020
Later among the works it cites.
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
Later among the works it cites.
B. Desplanques, J. Thienpondt, and K. Demuynck, “Ecapa-tdnn: Emphasized channel attention, propagation and aggregation in tdnn based speaker verification,” arXiv preprint arXiv:2005.07143 , 2020
Original
2020

Beyond the bibliography

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

Open on alphaXiv

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K. Okabe, T. Koshinaka, and K. Shinoda, “Attentive statistics pooling for deep speaker embedding,” arXiv preprint arXiv:1803.10963 , 2018

Original

2018

Cited alongside, same era.

J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2018, pp. 7132–7141

2018

Cited alongside, same era.

C.-C. Lo, S.-W. Fu, W.-C. Huang, X. Wang, J. Yamagishi, Y. Tsao, and H.-M. Wang, “Mosnet: Deep learning based objective assessment for voice conversion,” arXiv preprint arXiv:1904.08352 , 2019

Original

2019

Cited alongside, same era.

J. Yamagishi, C. Veaux, and K. MacDonald, “Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit,” 2019

2019

Cited alongside, same era.