A Spectral Energy Distance for Parallel Speech Synthesis

Understand

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 operations per second of generated audio, making them ill-suited for deployment on specialized deep learning hardware.
• Here, we propose a new learning method that allows us to train highly parallel models of speech, without requiring access to an analytical likelihood function.
• Our approach is based on a generalized energy distance between the distributions of the generated and real audio.