  Speech separation has been successfully applied as a frontend processing
module of conversation transcription systems thanks to its ability to handle
overlapped speech and its flexibility to combine with downstream tasks such as
automatic speech recognition (ASR). However, a speech separation model often
introduces target speech distortion, resulting in a sub-optimum word error rate
(WER). In this paper, we describe our efforts to improve the performance of a
single channel speech separation system. Specifically, we investigate a
two-stage training scheme that firstly applies a feature level optimization
criterion for pretraining, followed by an ASR-oriented optimization criterion
using an end-to-end (E2E) speech recognition model. Meanwhile, to keep the
model light-weight, we introduce a modified teacher-student learning technique
for model compression. By combining those approaches, we achieve a absolute
average WER improvement of 2.70% and 0.77% using models with less than 10M
parameters compared with the previous state-of-the-art results on the LibriCSS
dataset for utterance-wise evaluation and continuous evaluation, respectively
