Boosting Fast and High-Quality Speech Synthesis with Linear Diffusion

Understand

Denoising Diffusion Probabilistic Models have shown extraordinary ability on various generative tasks.

• However, their slow inference speed renders them impractical in speech synthesis.
• This paper proposes a linear diffusion model (LinDiff) based on an ordinary differential equation to simultaneously reach fast inference and high sample quality.
• Firstly, we employ linear interpolation between the target and noise to design a diffusion sequence for training, while previously the diffusion path that links the noise and target is a curved segment.