  Speaker separation aims to extract multiple voices from a mixed signal. In
this paper, we propose two speaker-aware designs to improve the existing
speaker separation solutions. The first model is a speaker conditioning network
that integrates speech samples to generate individualized speaker conditions,
which then provide informed guidance for a separation module to produce
well-separated outputs.
  The second design aims to reduce non-target voices in the separated speech.
To this end, we propose negative distances to penalize the appearance of any
non-target voice in the channel outputs, and positive distances to drive the
separated voices closer to the clean targets. We explore two different setups,
weighted-sum and triplet-like, to integrate these two distances to form a
combined auxiliary loss for the separation networks. Experiments conducted on
LibriMix demonstrate the effectiveness of our proposed models.
