We propose SE-Bridge, a novel method for speech enhancement (SE). After
recently applying the diffusion models to speech enhancement, we can achieve
speech enhancement by solving a stochastic differential equation (SDE). Each
SDE corresponds to a probabilistic flow ordinary differential equation
(PF-ODE), and the trajectory of the PF-ODE solution consists of the speech
states at different moments. Our approach is based on consistency model that
ensure any speech states on the same PF-ODE trajectory, correspond to the same
initial state. By integrating the Brownian Bridge process, the model is able to
generate high-intelligibility speech samples without adversarial training. This
is the first attempt that applies the consistency models to SE task, achieving
state-of-the-art results in several metrics while saving 15 x the time required
for sampling compared to the diffusion-based baseline. Our experiments on
multiple datasets demonstrate the effectiveness of SE-Bridge in SE.
Furthermore, we show through extensive experiments on downstream tasks,
including Automatic Speech Recognition (ASR) and Speaker Verification (SV),
that SE-Bridge can effectively support multiple downstream tasks.

SE-Bridge 是一种基于稳定性模型的语音增强方法，通过解决一个随机微分方程并结合布朗桥过程，能够在多种指标上达到最先进的效果，并提高 15 倍的采样效率，同时在 ASR 和 SV 等下游任务中也表现出了良好的性能。