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.

翻译：摘要：尽管基于深度神经网络（DNN）的语音增强方法在性能上优于传统的非DNN方法，但这类方法往往会降低生成输出的感知质量。为解决此问题，我们提出了一种名为Diffiner的基于DNN的生成式精炼器，旨在提升经语音增强方法预处理后的语音感知质量。我们仅利用纯净语音数据集训练了一个基于扩散的生成模型。随后，该精炼器通过去噪扩散修复技术生成纯净语音分量，并将其有效混合至前期语音增强方法导致的退化与失真部分，从而获得精炼语音。一旦我们的精炼器在纯净语音集上完成训练，即可应用于多种语音增强方法，无需针对特定增强模块进行额外训练。因此，该精炼器可作为面向不同语音增强方法的通用后处理模块，并在模块化方面展现出巨大潜力。实验结果表明，无论采用何种前置语音增强方法，我们的方法均能有效提升语音感知质量。