提出快速求解器，10次神经网络计算即可完成语音修复。
A Fast Solver for Interpolating Stochastic Differential Equation Diffusion Models for Speech Restoration
- 构建可统一建模的插值随机微分方程框架
- 在多个语音任务中实现仅需10次前向计算的快速采样
- 特别适合需要高效生成的语音修复场景
扩散概率模型（DPMs）是无条件图像生成中的成熟方法，而SGMSE+是用于语音增强的成熟条件扩散模型。扩散模型的一个缺点是反向过程需要多次评估大型神经网络。尽管已开发出先进的快速采样求解器用于DPMs，但它们不直接适用于如SGMSE+这类模型，因为其扩散过程存在差异：DPMs在数据分布与标准高斯分布之间转换，而SGMSE+则在目标分布与噪声观测之间进行插值。本文首次建立了包含SGMSE+的插值随机微分方程（iSDE）的形式化框架，并提出一种针对iSDE的求解器。该求解器在多个语音修复任务中实现了仅需10次神经网络评估的快速采样。
原文摘要 · Abstract (English)
Diffusion Probabilistic Models (DPMs) are a well-established class of diffusion models for unconditional image generation, while SGMSE+ is a well-established conditional diffusion model for speech enhancement. One of the downsides of diffusion models is that solving the reverse process requires many evaluations of a large Neural Network. Although advanced fast sampling solvers have been developed for DPMs, they are not directly applicable to models such as SGMSE+ due to differences in their diffusion processes. Specifically, DPMs transform between the data distribution and a standard Gaussian distribution, whereas SGMSE+ interpolates between the target distribution and a noisy observation. This work first develops a formalism of interpolating Stochastic Differential Equations (iSDEs) that includes SGMSE+, and second proposes a solver for iSDEs. The proposed solver enables fast sampling with as few as 10 Neural Network evaluations across multiple speech restoration tasks.
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