Diffusion-based speech enhancement has shown promising results, but can
suffer from a slower inference time. Initializing the diffusion process with
the enhanced audio generated by a regression-based model can be used to reduce
the computational steps required. However, these approaches often necessitate a
regression model, further increasing the system's complexity. We propose
Thunder, a unified regression-diffusion model that utilizes the Brownian bridge
process which can allow the model to act in both modes. The regression mode can
be accessed by setting the diffusion time step closed to 1. However, the
standard score-based diffusion modeling does not perform well in this setup due
to gradient instability. To mitigate this problem, we modify the diffusion
model to predict the clean speech instead of the score function, achieving
competitive performance with a more compact model size and fewer reverse steps.

通过使用基于扩散的语音增强方法，初始化扩散过程以改进音频，并使用回归模型可以减少计算步骤，提出了一种名为 Thunder 的统一回归 - 扩散模型，该模型利用布朗桥过程可在两种模式下运行，通过将扩散时间步骤设置为接近 1 可访问回归模式，但是标准得分扩散建模在此设置中性能不佳，为了缓解这个问题，我们修改了扩散模型以预测干净的语音而不是得分函数，从而以更紧凑的模型大小和更少的反向步骤实现了具有竞争力的性能。

Thunder: 一种使用布朗桥进行单向反演步骤的统一回归扩散语音增强方法

Thunder : Unified Regression-Diffusion Speech Enhancement with a Single  Reverse Step using Brownian Bridge

Diffusion-based speech enhancement (SE) has been investigated recently, but
its decoding is very time-consuming. One solution is to initialize the decoding
process with the enhanced feature estimated by a predictive SE system. However,
this two-stage method ignores the complementarity between predictive and
diffusion SE. In this paper, we propose a unified system that integrates these
two SE modules. The system encodes both generative and predictive information,
and then applies both generative and predictive decoders, whose outputs are
fused. Specifically, the two SE modules are fused in the first and final
diffusion steps: the first step fusion initializes the diffusion process with
the predictive SE for improving the convergence, and the final step fusion
combines the two complementary SE outputs to improve the SE performance.
Experiments on the Voice-Bank dataset show that the diffusion score estimation
can benefit from the predictive information and speed up the decoding.

本文提出一种整合预测语音增强和扩散语音增强的统一系统，通过编码两者信息，并应用生成和预测解码器，最终将两种方法的输出融合，实现语音增强。实验证明，该方法可加快解码速度并提高性能。