  This paper summarizes several follow-up contributions for improving our
submitted NWPU speaker-dependent system for CHiME-5 challenge, which aims to
solve the problem of multi-channel, highly-overlapped conversational speech
recognition in a dinner party scenario with reverberations and non-stationary
noises. We adopt a speaker-aware training method by using i-vector as the
target speaker information for multi-talker speech separation. With only one
unified separation model for all speakers, we achieve a 10\% absolute
improvement in terms of word error rate (WER) over the previous baseline of
80.28\% on the development set by leveraging our newly proposed data processing
techniques and beamforming approach. With our improved back-end acoustic model,
we further reduce WER to 60.15\% which surpasses the result of our submitted
CHiME-5 challenge system without applying any fusion techniques.
