Fine-Grained Style Control in VITS-Based Text-to-Speech Synthesis

Author:

Huihang Zhong,Ke Dengfeng,Ya Li,Yao Wenhan,Bao Wenqian

Publisher

Springer Nature Singapore

Reference13 articles.

1. Shen, J., et al.: Natural TTS synthesis by conditioning Wavenet on MEL spectrogram predictions. In: 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 4779–4783. IEEE (2018)

2. Ren, Y., et al.: Fastspeech: fast, robust and controllable text to speech. In: Advances in Neural Information Processing Systems, vol. 32 (2019)

3. Ren, Y., et al.: FastSpeech 2: fast and high-quality end-to-end text to speech. In: International Conference on Learning Representations (2021). https://openreview.net/forum?id=piLPYqxtWuA

4. Kim, J., Kong, J., Son, J.: Conditional variational autoencoder with adversarial learning for end-to-end text-to-speech. In: International Conference on Machine Learning, pp. 5530–5540. PMLR (2021)

5. Wang, Y., et al.: Style tokens: unsupervised style modeling, control and transfer in end-to-end speech synthesis. In: International Conference on Machine Learning, pp. 5180–5189. PMLR (2018)

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