Large, pre-trained representation models trained using self-supervised
learning have gained popularity in various fields of machine learning because
they are able to extract high-quality salient features from input data. As
such, they have been frequently used as base networks for various pattern
classification tasks such as speech recognition. However, not much research has
been conducted on applying these types of models to the field of speech signal
generation. In this paper, we investigate the feasibility of using pre-trained
speech representation models for a downstream speech enhancement task. To
alleviate mismatches between the input features of the pre-trained model and
the target enhancement model, we adopt a novel feature normalization technique
to smoothly link these modules together. Our proposed method enables
significant improvements in speech quality compared to baselines when combined
with various types of pre-trained speech models.

本研究探讨了使用预训练语音表示模型进行下游语音增强任务的可行性，并提出一种新的特征归一化技术，将这些模块平滑地链接在一起，能够在结合各种类型的预训练语音模型时显著提高语音质量。