  Automatic transcription of meetings requires handling of overlapped speech,
which calls for continuous speech separation (CSS) systems. The uPIT criterion
was proposed for utterance-level separation with neural networks and introduces
the constraint that the total number of speakers must not exceed the number of
output channels. When processing meeting-like data in a segment-wise manner,
i.e., by separating overlapping segments independently and stitching adjacent
segments to continuous output streams, this constraint has to be fulfilled for
any segment. In this contribution, we show that this constraint can be
significantly relaxed. We propose a novel graph-based PIT criterion, which
casts the assignment of utterances to output channels in a graph coloring
problem. It only requires that the number of concurrently active speakers must
not exceed the number of output channels. As a consequence, the system can
process an arbitrary number of speakers and arbitrarily long segments and thus
can handle more diverse scenarios. Further, the stitching algorithm for
obtaining a consistent output order in neighboring segments is of less
importance and can even be eliminated completely, not the least reducing the
computational effort. Experiments on meeting-style WSJ data show improvements
in recognition performance over using the uPIT criterion.
