  Utterance-level permutation invariant training (uPIT) has achieved promising
progress on single-channel multi-talker speech separation task. Long short-term
memory (LSTM) and bidirectional LSTM (BLSTM) are widely used as the separation
networks of uPIT, i.e. uPIT-LSTM and uPIT-BLSTM. uPIT-LSTM has lower latency
but worse performance, while uPIT-BLSTM has better performance but higher
latency. In this paper, we propose using latency-controlled BLSTM (LC-BLSTM)
during inference to fulfill low-latency and good-performance speech separation.
To find a better training strategy for BLSTM-based separation network,
chunk-level PIT (cPIT) and uPIT are compared. The experimental results show
that uPIT outperforms cPIT when LC-BLSTM is used during inference. It is also
found that the inter-chunk speaker tracing (ST) can further improve the
separation performance of uPIT-LC-BLSTM. Evaluated on the WSJ0 two-talker
mixed-speech separation task, the absolute gap of signal-to-distortion ratio
(SDR) between uPIT-BLSTM and uPIT-LC-BLSTM is reduced to within 0.7 dB.
