  Speech separation, the task of isolating multiple speech sources from a mixed
audio signal, remains challenging in noisy environments. In this paper, we
propose a generative correction method to enhance the output of a
discriminative separator. By leveraging a generative corrector based on a
diffusion model, we refine the separation process for single-channel mixture
speech by removing noises and perceptually unnatural distortions. Furthermore,
we optimize the generative model using a predictive loss to streamline the
diffusion model's reverse process into a single step and rectify any associated
errors by the reverse process. Our method achieves state-of-the-art performance
on the in-domain Libri2Mix noisy dataset, and out-of-domain WSJ with a variety
of noises, improving SI-SNR by 22-35% relative to SepFormer, demonstrating
robustness and strong generalization capabilities.
