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arXiv · 1803.09017

Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis

Yuxuan Wang·Daisy Stanton·Yu Zhang·RJ Skerry-Ryan·Eric Battenberg·Joel Shor·Ying Xiao·Fei Ren·Ye Jia·Rif A. Saurous
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Abstract

In this work, we propose "global style tokens" (GSTs), a bank of embeddings that are jointly trained within Tacotron, a state-of-the-art end-to-end speech synthesis system. The embeddings are trained with no explicit labels, yet learn to model a large range of acoustic expressiveness. GSTs lead to a rich set of significant results. The soft interpretable "labels" they generate can be used to control synthesis in novel ways, such as varying speed and speaking style - independently of the text content. They can also be used for style transfer, replicating the speaking style of a single audio clip across an entire long-form text corpus. When trained on noisy, unlabeled found data, GSTs learn to factorize noise and speaker identity, providing a path towards highly scalable but robust speech synthesis.

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Yuxuan Wang, Daisy Stanton, Yu Zhang, RJ Skerry-Ryan, Eric Battenberg, Joel Shor, Ying Xiao, Fei Ren, Ye Jia, Rif A. Saurous. 2018-03-23. Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis. https://arxiv.org/abs/1803.09017

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