A Spectral Energy Distance for Parallel Speech Synthesis

• URL: http://arxiv.org/abs/2008.01160v2
• Date: Fri, 23 Oct 2020 11:44:08 GMT
• Title: A Spectral Energy Distance for Parallel Speech Synthesis
• Authors: Alexey A. Gritsenko, Tim Salimans, Rianne van den Berg, Jasper Snoek, Nal Kalchbrenner
• Abstract summary: Speech synthesis is an important practical generative modeling problem. We propose a new learning method that allows us to train highly parallel models of speech.
• Score: 29.14723501889278
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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 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. This spectral energy distance is a proper scoring rule with respect to the distribution over magnitude-spectrograms of the generated waveform audio and offers statistical consistency guarantees. The distance can be calculated from minibatches without bias, and does not involve adversarial learning, yielding a stable and consistent method for training implicit generative models. Empirically, we achieve state-of-the-art generation quality among implicit generative models, as judged by the recently-proposed cFDSD metric. When combining our method with adversarial techniques, we also improve upon the recently-proposed GAN-TTS model in terms of Mean Opinion Score as judged by trained human evaluators.

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