The current monaural state of the art tools for speech separation relies on supervised learning. This means that they must deal with permutation problem, they are impacted by the mismatch on the number of speakers used in training and inference. Moreover, their performance heavily relies on the presence of high-quality labelled data. These problems can be effectively addressed by employing a fully unsupervised technique for speech separation. In this paper, we use contrastive learning to establish the representations of frames then use the learned representations in the downstream deep modularization task. Concretely, we demonstrate experimentally that in speech separation, different frames of a speaker can be viewed as augmentations of a given hidden standard frame of that speaker. The frames of a speaker contain enough prosodic information overlap which is key in speech separation. Based on this, we implement a self-supervised learning to learn to minimize the distance between frames belonging to a given speaker. The learned representations are used in a downstream deep modularization task to cluster frames based on speaker identity. Evaluation of the developed technique on WSJ0-2mix and WSJ0-3mix shows that the technique attains SI-SNRi and SDRi of 20.8 and 21.0 respectively in WSJ0-2mix. In WSJ0-3mix, it attains SI-SNRi and SDRi of 20.7 and 20.7 respectively in WSJ0-2mix. Its greatest strength being that as the number of speakers increase, its performance does not degrade significantly.

翻译：当前单声道语音分离的最先进技术依赖于监督学习。这意味着它们必须应对排列问题，且受训练与推理中说话人数量不匹配的影响。此外，它们的性能高度依赖于高质量标注数据的存在。通过采用完全无监督的语音分离技术，这些问题可以得到有效解决。本文利用对比学习建立帧的表示，并将学习到的表示用于下游深度模块化任务。具体而言，我们通过实验证明，在语音分离中，同一说话人的不同帧可被视为该说话人特定隐含标准帧的增强。说话人的帧包含足够多的韵律信息重叠，这对语音分离至关重要。基于此，我们实现自监督学习，以最小化属于同一说话人的帧之间的距离。学习到的表示被用于下游深度模块化任务，以根据说话人身份对帧进行聚类。在WSJ0-2mix和WSJ0-3mix数据集上评估所开发的技术表明，该技术在WSJ0-2mix上分别达到20.8和21.0的SI-SNRi与SDRi，在WSJ0-3mix上分别达到20.7和20.7的SI-SNRi与SDRi。其最大优势在于，随着说话人数量的增加，其性能不会显著下降。