Diffusion-based Signal Refiner for Speech Enhancement and Separation

Although recent speech processing technologies have achieved significant improvements in objective metrics, there still remains a gap in human perceptual quality. This paper proposes Diffiner, a novel solution that utilizes the powerful generative capability of diffusion models' prior distributions to address this fundamental issue. Diffiner leverages the probabilistic generative framework of diffusion models and learns natural prior distributions of clean speech to convert outputs from existing speech processing systems into perceptually natural high-quality audio. In contrast to conventional deterministic approaches, our method simultaneously analyzes both the original degraded speech and the pre-processed speech to accurately identify unnatural artifacts introduced during processing. Then, through the iterative sampling process of the diffusion model, these degraded portions are replaced with perceptually natural and high-quality speech segments. Experimental results indicate that Diffiner can recover a clearer harmonic structure of speech, which is shown to result in improved perceptual quality w.r.t. several metrics as well as in a human listening test. This highlights Diffiner's efficacy as a versatile post-processor for enhancing existing speech processing pipelines.