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This paper presents a novel design of neural network system for fine-grained style modeling, transfer and prediction in expressive text-to-speech (TTS) synthesis.
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K. Qian, Y. Zhang, S. Chang, X. Yang, and M. Hasegawa-Johnson, “Autovc: Zero-shot voice style transfer with only autoencoder loss,” in International Conference on Machine Learning . PMLR, 2019, pp. 5210–5219
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Y.-J. Zhang, S. Pan, L. He, and Z.-H. Ling, “Learning latent representations for style control and transfer in end-to-end speech synthesis,” in ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2019, pp. 6945–6949
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T. Kenter, V. Wan, C.-A. Chan, R. Clark, and J. Vit, “Chive: Varying prosody in speech synthesis with a linguistically driven dynamic hierarchical conditional variational network,” in International Conference on Machine Learning , 2019, pp. 3331–3340
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G. Zhang, Y. Qin, and T. Lee, “Learning syllable-level discrete prosodic representation for expressive speech generation,” Proc. Interspeech 2020 , pp. 3426–3430, 2020
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