  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.
