  Finding synthetic artifacts of spoofing data will help the anti-spoofing
countermeasures (CMs) system discriminate between spoofed and real speech. The
Conformer combines the best of convolutional neural network and the
Transformer, allowing it to aggregate global and local information. This may
benefit the CM system to capture the synthetic artifacts hidden both locally
and globally. In this paper, we present the transfer learning based
MFA-Conformer structure for CM systems. By pre-training the Conformer encoder
with different tasks, the robustness of the CM system is enhanced. The proposed
method is evaluated on both Chinese and English spoofing detection databases.
In the FAD clean set, proposed method achieves an EER of 0.04%, which
dramatically outperforms the baseline. Our system is also comparable to the
pre-training methods base on Wav2Vec 2.0. Moreover, we also provide a detailed
analysis of the robustness of different models.
