  The previous SpEx+ has yielded outstanding performance in speaker extraction
and attracted much attention. However, it still encounters inadequate
utilization of multi-scale information and speaker embedding. To this end, this
paper proposes a new effective speaker extraction system with multi-scale
interfusion and conditional speaker modulation (ConSM), which is called
MC-SpEx. First of all, we design the weight-share multi-scale fusers
(ScaleFusers) for efficiently leveraging multi-scale information as well as
ensuring consistency of the model's feature space. Then, to consider different
scale information while generating masks, the multi-scale interactive mask
generator (ScaleInterMG) is presented. Moreover, we introduce ConSM module to
fully exploit speaker embedding in the speech extractor. Experimental results
on the Libri2Mix dataset demonstrate the effectiveness of our improvements and
the state-of-the-art performance of our proposed MC-SpEx.
