  Speech separation has been well developed, with the very successful
permutation invariant training (PIT) approach, although the frequent label
assignment switching happening during PIT training remains to be a problem when
better convergence speed and achievable performance are desired. In this paper,
we propose to perform self-supervised pre-training to stabilize the label
assignment in training the speech separation model. Experiments over several
types of self-supervised approaches, several typical speech separation models
and two different datasets showed that very good improvements are achievable if
a proper self-supervised approach is chosen.
