  In far-field speaker verification, the performance of speaker embeddings is
susceptible to degradation when there is a mismatch between the conditions of
enrollment and test speech. To solve this problem, we propose the feature-level
and instance-level transfer learning in the teacher-student framework to learn
a domain-invariant embedding space. For the feature-level knowledge transfer,
we develop the contrastive loss to transfer knowledge from teacher model to
student model, which can not only decrease the intra-class distance, but also
enlarge the inter-class distance. Moreover, we propose the instance-level
pairwise distance transfer method to force the student model to preserve
pairwise instances distance from the well optimized embedding space of the
teacher model. On FFSVC 2020 evaluation set, our EER on Full-eval trials is
relatively reduced by 13.9% compared with the fusion system result on
Partial-eval trials of Task2. On Task1, compared with the winner's DenseNet
result on Partial-eval trials, our minDCF on Full-eval trials is relatively
reduced by 6.3%. On Task3, the EER and minDCF of our proposed method on
Full-eval trials are very close to the result of the fusion system on
Partial-eval trials. Our results also outperform other competitive domain
adaptation methods.
