From Speaker Verification to Deepfake Algorithm Recognition: Our Learned Lessons from ADD2023 Track3

This paper presents our learned lessons from the ADD2023 track3, Deepfake Algorithm Recognition (AR). In recent years, speech synthesis has made remarkable progress, where it has become increasingly difficult for human listeners to differentiate between synthesized speech and genuine human speech. Previous research has demonstrated that improving the recognition of deepfake algorithms can significantly enhance spoofing detection. Therefore, the primary focus of this paper is to investigate deepfake algorithm recognition, with experiments conducted based on the ADD2023 challenge. Inspired by speaker verification, we approach deepfake algorithm recognition as an open-set task. We propose a center-based similarity maximum method for determining the category of deepfake algorithms. Finally, by combining the scores from multiple models at the score level, we achieve an impressive F1 score of 0.8312 on the evaluation set.

Duke Scholars

Author Ming Li DKU Faculty