Discrete-Time Diffusion-Like Models for Speech Synthesis
This addresses a specific limitation in speech synthesis for researchers and practitioners, but it is incremental as it builds on existing diffusion models.
The paper tackled the mismatch between continuous training and discrete sampling in diffusion models for speech synthesis by exploring discrete-time processes, achieving comparable speech quality with more efficient and consistent training and inference.
Diffusion models have attracted a lot of attention in recent years. These models view speech generation as a continuous-time process. For efficient training, this process is typically restricted to additive Gaussian noising, which is limiting. For inference, the time is typically discretized, leading to the mismatch between continuous training and discrete sampling conditions. Recently proposed discrete-time processes, on the other hand, usually do not have these limitations, may require substantially fewer inference steps, and are fully consistent between training/inference conditions. This paper explores some diffusion-like discrete-time processes and proposes some new variants. These include processes applying additive Gaussian noise, multiplicative Gaussian noise, blurring noise and a mixture of blurring and Gaussian noises. The experimental results suggest that discrete-time processes offer comparable subjective and objective speech quality to their widely popular continuous counterpart, with more efficient and consistent training and inference schemas.