StoRM: A Diffusion-based Stochastic Regeneration Model for Speech Enhancement and Dereverberation

arXiv:2212.11851v2 Announce Type: replace-cross
Abstract: Diffusion models have shown a great ability at bridging the performance gap between predictive and generative approaches for speech enhancement. We have shown that they may even outperform their predictive counterparts for non-additive corruption types or when they are evaluated on mismatched conditions. However, diffusion models suffer from a high computational burden, mainly as they require to run a neural network for each reverse diffusion step, whereas predictive approaches only require one pass. As diffusion …

arxiv cs.lg cs.sd diffusion eess.as speech stochastic storm type