  The continuous speech separation (CSS) is a task to separate the speech
sources from a long, partially overlapped recording, which involves a varying
number of speakers. A straightforward extension of conventional utterance-level
speech separation to the CSS task is to segment the long recording with a
size-fixed window and process each window separately. Though effective, this
extension fails to model the long dependency in speech and thus leads to
sub-optimum performance. The recent proposed dual-path modeling could be a
remedy to this problem, thanks to its capability in jointly modeling the
cross-window dependency and the local-window processing. In this work, we
further extend the dual-path modeling framework for CSS task. A
transformer-based dual-path system is proposed, which integrates transform
layers for global modeling. The proposed models are applied to LibriCSS, a real
recorded multi-talk dataset, and consistent WER reduction can be observed in
the ASR evaluation for separated speech. Also, a dual-path transformer equipped
with convolutional layers is proposed. It significantly reduces the computation
amount by 30% with better WER evaluation. Furthermore, the online processing
dual-path models are investigated, which shows 10% relative WER reduction
compared to the baseline.
