  We propose a training scheme to train neural network-based source separation
algorithms from scratch when parallel clean data is unavailable. In particular,
we demonstrate that an unsupervised spatial clustering algorithm is sufficient
to guide the training of a deep clustering system. We argue that previous work
on deep clustering requires strong supervision and elaborate on why this is a
limitation. We demonstrate that (a) the single-channel deep clustering system
trained according to the proposed scheme alone is able to achieve a similar
performance as the multi-channel teacher in terms of word error rates and (b)
initializing the spatial clustering approach with the deep clustering result
yields a relative word error rate reduction of 26 % over the unsupervised
teacher.
