  Although deep neural network (DNN)-based speech enhancement (SE) methods
outperform the previous non-DNN-based ones, they often degrade the perceptual
quality of generated outputs. To tackle this problem, we introduce a DNN-based
generative refiner, Diffiner, aiming to improve perceptual speech quality
pre-processed by an SE method. We train a diffusion-based generative model by
utilizing a dataset consisting of clean speech only. Then, our refiner
effectively mixes clean parts newly generated via denoising diffusion
restoration into the degraded and distorted parts caused by a preceding SE
method, resulting in refined speech. Once our refiner is trained on a set of
clean speech, it can be applied to various SE methods without additional
training specialized for each SE module. Therefore, our refiner can be a
versatile post-processing module w.r.t. SE methods and has high potential in
terms of modularity. Experimental results show that our method improved
perceptual speech quality regardless of the preceding SE methods used.
