  Knowledge distillation (KD) is used to enhance automatic speaker verification
performance by ensuring consistency between large teacher networks and
lightweight student networks at the embedding level or label level. However,
the conventional label-level KD overlooks the significant knowledge from
non-target speakers, particularly their classification probabilities, which can
be crucial for automatic speaker verification. In this paper, we first
demonstrate that leveraging a larger number of training non-target speakers
improves the performance of automatic speaker verification models. Inspired by
this finding about the importance of non-target speakers' knowledge, we
modified the conventional label-level KD by disentangling and emphasizing the
classification probabilities of non-target speakers during knowledge
distillation. The proposed method is applied to three different student model
architectures and achieves an average of 13.67% improvement in EER on the
VoxCeleb dataset compared to embedding-level and conventional label-level KD
methods.
