A Spectral Energy Distance for Parallel Speech Synthesis

NIPS 2020, 2020.

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We propose a new learning method that allows us to train highly parallel models of speech, without requiring access to an analytical likelihood function

Abstract:

Speech synthesis is an important practical generative modeling problem that has seen great progress over the last few years, with likelihood-based autoregressive neural models now outperforming traditional concatenative systems. A downside of such autoregressive models is that they require executing tens of thousands of sequential opera...More
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