Built on

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
Original
1904
Earlier work this paper cites.
S. Choi, S. Han, D. Kim, and S. Ha, “Attentron: Few-shot text-to-speech utilizing attention-based variable-length embedding,” arXiv preprint arXiv:2005.08484
Original
2005
Earlier work this paper 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
Original
2006
Earlier work this paper cites.
J. Donahue, S. Dieleman, M. Bińkowski, E. Elsen, and K. Simonyan, “End-to-end adversarial text-to-speech,” arXiv preprint arXiv:2006.03575
Original
2006
Earlier work this paper cites.
J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-attentive tacotron: Robust and controllable neural tts synthesis including unsupervised duration modeling,” arXiv preprint arXiv:2010.04301
Original
2010
Earlier work this paper cites.

Similar

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)
2018
Cited alongside, same era.
L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized end-to-end loss for speaker verification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2018
Cited alongside, same era.
J.-M. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
Cited alongside, same era.
R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019

Then

W.-N. Hsu, Y. Zhang, R. J. Weiss, Y.-A. Chung, Y. Wang, Y. Wu, and J. Glass, “Disentangling correlated speaker and noise for speech synthesis via data augmentation and adversarial factorization,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
Later among the works it cites.
G. Sun, Y. Zhang, R. J. Weiss, Y. Cao, H. Zen, and Y. Wu, “Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis,” in ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP)
2020
Later among the works it cites.
E. Tzinis, Z. Wang, and P. Smaragdis, “Sudo rm-rf: Efficient networks for universal audio source separation,” in 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
2020
Later among the works it cites.
C. Zhang, Y. Ren, X. Tan, J. Liu, K. Zhang, T. Qin, S. Zhao, and T.-Y. Liu, “Denoispeech: Denoising text to speech with frame-level noise modeling,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Reading the bibliography…

2022

Fine-grained Noise Control for Multispeaker Speech Synthesis

Nikitaras, Karolos, Vamvoukakis, Georgios, Ellinas, Nikolaos et al.

Understand

A text-to-speech (TTS) model typically factorizes speech attributes such as content, speaker and prosody into disentangled representations.Recent works aim to additionally model the acoustic conditions explicitly, in order to disentangle the primary speech factors, i.e.

linguistic content, prosody and timbre from any residual factors, such as recording conditions and background noise.This paper proposes unsupervised, interpretable and fine-grained noise and prosody modeling.
We incorporate adversarial training, representation bottleneck and utterance-to-frame modeling in order to learn frame-level noise representations.
To the same end, we perform fine-grained prosody modeling via a Fully Hierarchical Variational AutoEncoder (FVAE) which additionally results in more expressive speech synthesis.

Built on

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
Original
1904
Earlier work this paper cites.
S. Choi, S. Han, D. Kim, and S. Ha, “Attentron: Few-shot text-to-speech utilizing attention-based variable-length embedding,” arXiv preprint arXiv:2005.08484
Original
2005
Earlier work this paper 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
Original
2006
Earlier work this paper cites.
J. Donahue, S. Dieleman, M. Bińkowski, E. Elsen, and K. Simonyan, “End-to-end adversarial text-to-speech,” arXiv preprint arXiv:2006.03575
Original
2006
Earlier work this paper cites.
J. Shen, Y. Jia, M. Chrzanowski, Y. Zhang, I. Elias, H. Zen, and Y. Wu, “Non-attentive tacotron: Robust and controllable neural tts synthesis including unsupervised duration modeling,” arXiv preprint arXiv:2010.04301
Original
2010
Earlier work this paper cites.

Similar

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)
2018
Cited alongside, same era.
L. Wan, Q. Wang, A. Papir, and I. L. Moreno, “Generalized end-to-end loss for speaker verification,” in 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2018
Cited alongside, same era.
J.-M. Valin and J. Skoglund, “Lpcnet: Improving neural speech synthesis through linear prediction,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
Cited alongside, same era.
R. Prenger, R. Valle, and B. Catanzaro, “Waveglow: A flow-based generative network for speech synthesis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019

Then

W.-N. Hsu, Y. Zhang, R. J. Weiss, Y.-A. Chung, Y. Wang, Y. Wu, and J. Glass, “Disentangling correlated speaker and noise for speech synthesis via data augmentation and adversarial factorization,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)
2019
Later among the works it cites.
G. Sun, Y. Zhang, R. J. Weiss, Y. Cao, H. Zen, and Y. Wu, “Fully-hierarchical fine-grained prosody modeling for interpretable speech synthesis,” in ICASSP 2020-2020 IEEE international conference on acoustics, speech and signal processing (ICASSP)
2020
Later among the works it cites.
E. Tzinis, Z. Wang, and P. Smaragdis, “Sudo rm-rf: Efficient networks for universal audio source separation,” in 2020 IEEE 30th International Workshop on Machine Learning for Signal Processing (MLSP)
2020
Later among the works it cites.
C. Zhang, Y. Ren, X. Tan, J. Liu, K. Zhang, T. Qin, S. Zhao, and T.-Y. Liu, “Denoispeech: Denoising text to speech with frame-level noise modeling,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

Beyond the bibliography

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

Open on alphaXiv

alphaXiv is searching for related work…

Cited alongside, same era.

J. Kim, J. Kong, and J. Son, “Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech,” in International Conference on Machine Learning

Cited in the paper.

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,” arXiv preprint arXiv:1609.03499

Original

Cited in the paper.

J. Yamagishi, C. Veaux, K. MacDonald et al. , “Cstr vctk corpus: English multi-speaker corpus for cstr voice cloning toolkit (version 0.92).”

Cited in the paper.

C. Kim and R. M. Stern, “Robust signal-to-noise ratio estimation based on waveform amplitude distribution analysis,” in Ninth Annual Conference of the International Speech Communication Association

Cited in the paper.

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

Cited in the paper.

K. Lee, K. Park, and D. Kim, “Styler: Style factor modeling with rapidity and robustness via speech decomposition for expressive and controllable neural text to speech,” arXiv preprint arXiv:2103.09474

Original

Cited in the paper.

C. Valentini-Botinhao and J. Yamagishi, “Speech enhancement of noisy and reverberant speech for text-to-speech,” IEEE/ACM Transactions on Audio, Speech, and Language Processing

Cited in the paper.

O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical image computing and computer-assisted intervention

Cited in the paper.

2021

Later among the works it cites.