Text-driven Emotional Style Control and Cross-speaker Style Transfer in Neural TTS

Y. Lee, A. Rabiee, and S.-Y. Lee, “Emotional end-to-end neural speech synthesizer,” arXiv preprint arXiv:1711.05447 , 2017

Original

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.

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.

J. Devlin, M.-W. Chang, K. Lee, and K. Toutanova, “Bert: Pre-training of deep bidirectional transformers for language understanding,” arXiv preprint arXiv:1810.04805 , 2018

Original

2018

Earlier work this paper cites.

A. v. d. Oord, Y. Li, and O. Vinyals, “Representation learning with contrastive predictive coding,” arXiv preprint arXiv:1807.03748 , 2018

Original

2018

Earlier work this paper cites.

N. Li, S. Liu, Y. Liu, S. Zhao, and M. Liu, “Neural speech synthesis with transformer network,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 33, no. 01, 2019, pp. 6706–6713

2019

Earlier work this paper cites.

M. Whitehill, S. Ma, D. McDuff, and Y. Song, “Multi-reference neural tts stylization with adversarial cycle consistency,” arXiv preprint arXiv:1910.11958 , 2019

Original

2019

Earlier work this paper cites.

Y. Bian, C. Chen, Y. Kang, and Z. Pan, “Multi-reference tacotron by intercross training for style disentangling, transfer and control in speech synthesis,” arXiv preprint arXiv:1904.02373 , 2019

Original

2019

Earlier work this paper cites.

N. Reimers and I. Gurevych, “Sentence-bert: Sentence embeddings using siamese bert-networks,” arXiv preprint arXiv:1908.10084 , 2019

Original

2019

Earlier work this paper cites.

R. Valle, K. Shih, R. Prenger, and B. Catanzaro, “Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis,” arXiv preprint arXiv:2005.05957 , 2020

Original

2020

Cited alongside, same era.

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

Cited alongside, same era.

J. Kim, S. Kim, J. Kong, and S. Yoon, “Glow-tts: A generative flow for text-to-speech via monotonic alignment search,” Advances in Neural Information Processing Systems , vol. 33, pp. 8067–8077, 2020

2020

Cited alongside, same era.

Y. Zhang, L. Deng, and Y. Wang, “Unified mandarin tts front-end based on distilled bert model,” arXiv preprint arXiv:2012.15404 , 2020

Original

2020

Cited alongside, same era.

T. Li, X. Wang, Q. Xie, Z. Wang, and L. Xie, “Controllable cross-speaker emotion transfer for end-to-end speech synthesis,” arXiv preprint arXiv:2109.06733 , 2021

Original

2021

Later among the works it cites.

S. Pan and L. He, “Cross-speaker style transfer with prosody bottleneck in neural speech synthesis,” arXiv preprint arXiv:2107.12562 , 2021

Original

2021

Later among the works it cites.

Y. Jia, H. Zen, J. Shen, Y. Zhang, and Y. Wu, “Png bert: augmented bert on phonemes and graphemes for neural tts,” arXiv preprint arXiv:2103.15060 , 2021

Original

2021

Later among the works it cites.

G. Xu, W. Song, Z. Zhang, C. Zhang, X. He, and B. Zhou, “Improving prosody modelling with cross-utterance bert embeddings for end-to-end speech synthesis,” in ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2021, pp. 6079–6083

2021

Later among the works it cites.