Fake Audio Detection Based On Unsupervised Pretraining Models
Zhiqiang Lv, Shanshan Zhang, Kai Tang, Pengfei Hu
Abstract
This work presents our systems for the ADD2022 challenge. The ADD2022 challenge is the first audio deep synthesis detection challenge, which aims to spot various kinds of fake audios. We have explored using unsupervised pretraining models to build fake audio detection systems. Results indicate that unsupervised pretraining models can achieve excellent performance for fake audio detection. Our final EER results for low-quality fake audio detection and partially fake audio detection are 32.80% and 4.80% relatively. For partially fake audio detection, our results ranked first in the competition. Even trained with totally mismatched data, our method still generalizes well for partially fake audio detection.