Recent work in the domain of speech enhancement has explored the use of
self-supervised speech representations to aid in the training of neural speech
enhancement models. However, much of this work focuses on using the deepest or
final outputs of self supervised speech representation models, rather than the
earlier feature encodings. The use of self supervised representations in such a
way is often not fully motivated. In this work it is shown that the distance
between the feature encodings of clean and noisy speech correlate strongly with
psychoacoustically motivated measures of speech quality and intelligibility, as
well as with human Mean Opinion Score (MOS) ratings. Experiments using this
distance as a loss function are performed and improved performance over the use
of STFT spectrogram distance based loss as well as other common loss functions
from speech enhancement literature is demonstrated using objective measures
such as perceptual evaluation of speech quality (PESQ) and short-time objective
intelligibility (STOI).

本文研究了语音增强中使用自我监督语音表示来帮助神经语音增强模型训练的方法，指出清洁和嘈杂语音特征编码之间的距离与语音质量和可懂性的心理声学测量值以及人类的平均意见分数（MOS）密切相关，并通过使用该距离作为损失函数的实验证明了相对于来自语音增强文献的常见损失函数的 STFT 频谱图距离损失，以及其他损失函数的性能。