  Deep-learning based speech separation models confront poor generalization
problem that even the state-of-the-art models could abruptly fail when
evaluating them in mismatch conditions. To address this problem, we propose an
easy-to-implement yet effective consistency based semi-supervised learning
(SSL) approach, namely Mixup-Breakdown training (MBT). It learns a teacher
model to "breakdown" unlabeled inputs, and the estimated separations are
interpolated to produce more useful pseudo "mixup" input-output pairs, on which
the consistency regularization could apply for learning a student model. In our
experiment, we evaluate MBT under various conditions with ascending degrees of
mismatch, including unseen interfering speech, noise, and music, and compare
MBT's generalization capability against state-of-the-art supervised learning
and SSL approaches. The result indicates that MBT significantly outperforms
several strong baselines with up to 13.77% relative SI-SNRi improvement.
Moreover, MBT only adds negligible computational overhead to standard training
schemes.
