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2017

Uncovering Latent Style Factors for Expressive Speech Synthesis

Wang, Yuxuan, Skerry-Ryan, RJ, Xiao, Ying et al.

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Prosodic modeling is a core problem in speech synthesis.

The key challenge is producing desirable prosody from textual input containing only phonetic information.
In this preliminary study, we introduce the concept of "style tokens" in Tacotron, a recently proposed end-to-end neural speech synthesis model.
Using style tokens, we aim to extract independent prosodic styles from training data.

Uncovering Latent Style Factors for Expressive Speech Synthesis · Around

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T. Nose, J. Yamagishi, T. Masuko, and T. Kobayashi, “A style control technique for HMM-based expressive speech synthesis,” IEICE Transcations on Information and Systems , vol. 90, no. 9, pp. 1406–1413, 2007
2007
Earlier work this paper cites.
F. Eyben, S. Buchholz, and N. Braunschweiler, “Unsupervised clustering of emotion and voice styles for expressive TTS,” in Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE International Conference on . IEEE, 2012, pp. 4009–4012
2012
Earlier work this paper cites.
D. Bahdanau, K. Cho, and Y. Bengio, “Neural machine translation by jointly learning to align and translate,” arXiv preprint arXiv:1409.0473 , 2014
Original
2014
Earlier work this paper cites.
A. Graves, G. Wayne, and I. Danihelka, “Neural turing machines,” arXiv preprint arXiv:1410.5401 , 2014
Original
2014
Earlier work this paper cites.
O. Watts, Z. Wu, and S. King, “Sentence-level control vectors for deep neural network speech synthesis,” in Proceedings Interspeech , 2015
2015
Earlier work this paper cites.

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