542 citations · 1.5k across the 44 of their papers we have counts for
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Hierarchical Generative Modeling for Controllable Speech Synthesis
Wei-Ning Hsu, Yu Zhang, Ron J. Weiss +9
This paper proposes a neural sequence-to-sequence text-to-speech (TTS) model which can control latent attributes in the generated speech that are rarely annotated in the training d…
Semi-Supervised Training for Improving Data Efficiency in End-to-End Speech Synthesis
Yu-An Chung, Yuxuan Wang, Wei-Ning Hsu +2
Although end-to-end text-to-speech (TTS) models such as Tacotron have shown excellent results, they typically require a sizable set of high-quality <text, audio> pairs for training…
Predicting Expressive Speaking Style From Text In End-To-End Speech Synthesis
Daisy Stanton, Yuxuan Wang, RJ Skerry-Ryan
Global Style Tokens (GSTs) are a recently-proposed method to learn latent disentangled representations of high-dimensional data. GSTs can be used within Tacotron, a state-of-the-ar…
Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron
RJ Skerry-Ryan, Eric Battenberg, Ying Xiao +6
We present an extension to the Tacotron speech synthesis architecture that learns a latent embedding space of prosody, derived from a reference acoustic representation containing t…
Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis
Yuxuan Wang, Daisy Stanton, Yu Zhang +7
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 emb…