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。

自我监督的语音表征损失函数及其在语音增强中的应用

Perceive and predict: self-supervised speech representation based loss functions for speech enhancement

Learning an encoding of feature vectors in terms of an over-complete
dictionary or a information geometric (Fisher vectors) construct is wide-spread
in statistical signal processing and computer vision. In content based
information retrieval using deep-learning classifiers, such encodings are
learnt on the flattened last layer, without adherence to the multi-linear
structure of the underlying feature tensor. We illustrate a variety of feature
encodings incl. sparse dictionary coding and Fisher vectors along with
proposing that a structured tensor factorization scheme enables us to perform
retrieval that can be at par, in terms of average precision, with Fisher vector
encoded image signatures. In short, we illustrate how structural constraints
increase retrieval fidelity.

通过使用张量分解的结构约束，可以使基于内容的图像检索具有与 Fisher 向量编码图像签名相当的检索结果。