ISCA Archive SPSC 2022
ISCA Archive SPSC 2022

Adversarial speaker distillation for countermeasure model on automatic speaker verification

Yen-Lun Liao, Xuanjun Chen, Chung-Che Wang, Jyh-Shing Roger Jang

The countermeasure (CM) model is developed to protect ASV systems from spoof attacks and prevent resulting personal information leakage in Automatic Speaker Verification (ASV) system. Based on practicality and security considerations, the CM model is usually deployed on edge devices, which have more limited computing resources and storage space than cloud-based systems, confining the model size under a limitation. To better trade off the CM model sizes and performance, we proposed an adversarial speaker distillation method, which is an improved version of knowledge distillation method combined with generalized end-to-end (GE2E) pre-training and adversarial fine-tuning. In the evaluation phase of the ASVspoof 2021 Logical Access task, our proposed adversarial speaker distillation ResNetSE (ASD-ResNetSE) model reaches 0.2695 min t-DCF and 3.54% EER. ASD-ResNetSE only used 22.5% of parameters and 19.4% of multiply and accumulate operands of ResNetSE model.


doi: 10.21437/SPSC.2022-6

Cite as: Liao, Y.-L., Chen, X., Wang, C.-C., Jang, J.-S.R. (2022) Adversarial speaker distillation for countermeasure model on automatic speaker verification. Proc. 2nd Symposium on Security and Privacy in Speech Communication, 30-34, doi: 10.21437/SPSC.2022-6

@inproceedings{liao22_spsc,
  author={Yen-Lun Liao and Xuanjun Chen and Chung-Che Wang and Jyh-Shing Roger Jang},
  title={{Adversarial speaker distillation for countermeasure model on automatic speaker verification}},
  year=2022,
  booktitle={Proc. 2nd Symposium on Security and Privacy in Speech Communication},
  pages={30--34},
  doi={10.21437/SPSC.2022-6}
}