Self-supervised learned models have been found to be very effective for
certain speech tasks such as automatic speech recognition, speaker
identification, keyword spotting and others. While the features are undeniably
useful in speech recognition and associated tasks, their utility in speech
enhancement systems is yet to be firmly established, and perhaps not properly
understood. In this paper, we investigate the uses of SSL representations for
single-channel speech enhancement in challenging conditions and find that they
add very little value for the enhancement task. Our constraints are designed
around on-device real-time speech enhancement -- model is causal, the compute
footprint is small. Additionally, we focus on low SNR conditions where such
models struggle to provide good enhancement. In order to systematically examine
how SSL representations impact performance of such enhancement models, we
propose a variety of techniques to utilize these embeddings which include
different forms of knowledge-distillation and pre-training.

通过对自监督学习表示进行研究，我们发现它们在挑战性条件下的单通道语音增强任务中几乎没有增加价值。为了系统地评估 SSL 表示对这些增强模型性能的影响，我们提出了多种利用这些嵌入的技术，包括不同形式的知识蒸馏和预训练。