  Target speaker extraction is to extract the target speaker, specified by
enrollment utterance, in an environment with other competing speakers.
Therefore, the task needs to solve two problems, speaker identification and
separation, at the same time. In this paper, we combine self-attention and
Recurrent Neural Networks (RNN). Further, we exploit various ways to combining
different auxiliary information with mixed representations. Experimental
results show that our proposed model achieves excellent performance on the task
of target speaker extraction.
