  While the performance of offline neural speech separation systems has been
greatly advanced by the recent development of novel neural network
architectures, there is typically an inevitable performance gap between the
systems and their online variants. In this paper, we investigate how RNN-based
offline neural speech separation systems can be changed into their online
counterparts while mitigating the performance degradation. We decompose or
reorganize the forward and backward RNN layers in a bidirectional RNN layer to
form an online path and an offline path, which enables the model to perform
both online and offline processing with a same set of model parameters. We
further introduce two training strategies for improving the online model via
either a pretrained offline model or a multitask training objective. Experiment
results show that compared to the online models that are trained from scratch,
the proposed layer decomposition and reorganization schemes and training
strategies can effectively mitigate the performance gap between two RNN-based
offline separation models and their online variants.
