DiffGAN-TTS: High-Fidelity and Efficient Text-to-Speech with Denoising Diffusion GANs. (arXiv:2201.11972v1 [eess.AS])

Denoising diffusion probabilistic models (DDPMs) are expressive generative
models that have been used to solve a variety of speech synthesis problems.
However, because of their high sampling costs, DDPMs are difficult to use in
real-time speech processing applications. In this paper, we introduce
DiffGAN-TTS, a novel DDPM-based text-to-speech (TTS) model achieving
high-fidelity and efficient speech synthesis. DiffGAN-TTS is based on denoising
diffusion generative adversarial networks (GANs), which adopt an
adversarially-trained expressive model to approximate the denoising
distribution. We show with …

arxiv gans speech text text-to-speech