  This paper proposes the target speaker enhancement based speaker verification
network (TASE-SVNet), an all neural model that couples target speaker
enhancement and speaker embedding extraction for robust speaker verification
(SV). Specifically, an enrollment speaker conditioned speech enhancement module
is employed as the front-end for extracting target speaker from its mixture
with interfering speakers and environmental noises. Compared with the
conventional target speaker enhancement models, nontarget speaker/interference
suppression should draw additional attention for SV. Therefore, an effective
nontarget speaker sampling strategy is explored. To improve speaker embedding
extraction with a light-weighted model, a teacher-student (T/S) training is
proposed to distill speaker discriminative information from large models to
small models. Iterative inference is investigated to address the noisy speaker
enrollment problem. We evaluate the proposed method on two SV tasks, i.e., one
heavily overlapped speech and the other one with comprehensive noise types in
vehicle environments. Experiments show significant and consistent improvements
in Equal Error Rate (EER) over the state-of-the-art baselines.
