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.
@article{arxiv.2406.07461,
title = {Noise-robust Speech Separation with Fast Generative Correction},
author = {Helin Wang and Jesus Villalba and Laureano Moro-Velazquez and Jiarui Hai and Thomas Thebaud and Najim Dehak},
journal= {arXiv preprint arXiv:2406.07461},
year = {2024}
}