  In this paper, we present a novel multi-channel speech extraction system to
simultaneously extract multiple clean individual sources from a mixture in
noisy and reverberant environments. The proposed method is built on an improved
multi-channel time-domain speech separation network which employs speaker
embeddings to identify and extract multiple targets without label permutation
ambiguity. To efficiently inform the speaker information to the extraction
model, we propose a new speaker conditioning mechanism by designing an
additional speaker branch for receiving external speaker embeddings.
Experiments on 2-channel WHAMR! data show that the proposed system improves by
9% relative the source separation performance over a strong multi-channel
baseline, and it increases the speech recognition accuracy by more than 16%
relative over the same baseline.
