  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.
