Diffusion models are receiving a growing interest for a variety of signal
generation tasks such as speech or music synthesis. WaveGrad, for example, is a
successful diffusion model that conditionally uses the mel spectrogram to guide
a diffusion process for the generation of high-fidelity audio. However, such
models face important challenges concerning the noise diffusion process for
training and inference, and they have difficulty generating high-quality speech
for speakers that were not seen during training. With the aim of minimizing the
conditioning error and increasing the efficiency of the noise diffusion
process, we propose in this paper a new scheme called GLA-Grad, which consists
in introducing a phase recovery algorithm such as the Griffin-Lim algorithm
(GLA) at each step of the regular diffusion process. Furthermore, it can be
directly applied to an already-trained waveform generation model, without
additional training or fine-tuning. We show that our algorithm outperforms
state-of-the-art diffusion models for speech generation, especially when
generating speech for a previously unseen target speaker.

我们提出了一种称为 GLA-Grad 的新方案，该方案在正常扩散过程的每个步骤中引入了相位恢复算法，以最小化条件错误并提高噪声扩散过程的效率，尤其在为先前未见过的目标演讲者生成语音时，我们的算法优于最先进的扩散模型。