ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification

Li Zhang, Qing Wang, Kong Aik Lee, Lei Xie, Haizhou Li

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 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.


doi: 10.21437/Interspeech.2021-1980

Cite as: Zhang, L., Wang, Q., Lee, K.A., Xie, L., Li, H. (2021) Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification. Proc. Interspeech 2021, 1094-1098, doi: 10.21437/Interspeech.2021-1980

@inproceedings{zhang21g_interspeech,
  author={Li Zhang and Qing Wang and Kong Aik Lee and Lei Xie and Haizhou Li},
  title={{Multi-Level Transfer Learning from Near-Field to Far-Field Speaker Verification}},
  year=2021,
  booktitle={Proc. Interspeech 2021},
  pages={1094--1098},
  doi={10.21437/Interspeech.2021-1980}
}