Speech separation seeks to separate individual speech signals from a speech mixture. Typically, most separation models are trained on synthetic data due to the unavailability of target reference in real-world cocktail party scenarios. As a result, there exists a domain gap between real and synthetic data when deploying speech separation models in real-world applications. In this paper, we propose a self-supervised domain-invariant pretrained (DIP) frontend that is exposed to mixture data without the need for target reference speech. The DIP frontend utilizes a Siamese network with two innovative pretext tasks, mixture predictive coding (MPC) and mixture invariant coding (MIC), to capture shared contextual cues between real and synthetic unlabeled mixtures. Subsequently, we freeze the DIP frontend as a feature extractor when training the downstream speech separation models on synthetic data. By pretraining the DIP frontend with the contextual cues, we expect that the speech separation skills learned from synthetic data can be effectively transferred to real data. To benefit from the DIP frontend, we introduce a novel separation pipeline to align the feature resolution of the separation models. We evaluate the speech separation quality on standard benchmarks and real-world datasets. The results confirm the superiority of our DIP frontend over existing speech separation models. This study underscores the potential of large-scale pretraining to enhance the quality and intelligibility of speech separation in real-world applications.

翻译：语音分离旨在从混合语音信号中分离出独立的语音信号。由于真实鸡尾酒会场景中缺乏目标参考语音，大多数分离模型通常在合成数据上进行训练。这导致语音分离模型在实际应用部署时，真实数据与合成数据之间存在领域差异。本文提出一种自监督的领域不变预训练前端，该前端仅需接触混合语音数据而无需目标参考语音。该前端采用孪生网络结构，通过混合预测编码与混合不变编码两项创新性预训练任务，捕获真实与合成无标注混合数据间的共享上下文线索。随后，在下游语音分离模型使用合成数据训练时，我们将该前端冻结为特征提取器。通过利用上下文线索进行预训练，我们期望从合成数据学习到的语音分离能力能有效迁移至真实数据。为充分发挥该前端的优势，我们提出一种新颖的分离流程以对齐分离模型的特征分辨率。我们在标准基准数据集和真实世界数据集上评估语音分离质量。实验结果证实了该前端相较于现有语音分离模型的优越性。本研究凸显了大规模预训练在提升真实场景语音分离质量与可懂度方面的潜力。