In this paper, we introduce a novel semi-supervised learning framework for end-to-end speech separation. The proposed method first uses mixtures of unseparated sources and the mixture invariant training (MixIT) criterion to train a teacher model. The teacher model then estimates separated sources that are used to train a student model with standard permutation invariant training (PIT). The student model can be fine-tuned with supervised data, i.e., paired artificial mixtures and clean speech sources, and further improved via model distillation. Experiments with single and multi channel mixtures show that the teacher-student training resolves the over-separation problem observed in the original MixIT method. Further, the semisupervised performance is comparable to a fully-supervised separation system trained using ten times the amount of supervised data.

翻译：在本文中,我们为终端到终端语音分离引入了一个新的半监督的半监督学习框架。 拟议的方法首先使用未分离源的混合物和混合变量培训标准来培训教师模型。 教师模型然后估算了用于培训学生模型并进行标准变异培训的学生模型的分离源。 学生模型可以与监管数据进行微调,即配对的人工混合物和清洁语音源,并通过模型蒸馏进一步改进。 单渠道和多渠道混合物的实验表明师生培训解决了最初的混合模块方法中观察到的过度分离问题。 此外,半监督性绩效可以与使用监管数据量十倍培训的完全监督的分离系统相比。