SZU-AFS Antispoofing System for the ASVspoof 5 Challenge
<|reference_start|>SZU-AFS Antispoofing System for the ASVspoof 5 Challenge: This paper presents the SZU-AFS anti-spoofing system, designed for Track 1 of the ASVspoof 5 Challenge under open conditions. The system is built with four stages: selecting a baseline model, exploring effective data augmentation (DA) methods for fine-tuning, applying a co-enhancement strategy based on gradient norm aware minimization (GAM) for secondary fine-tuning, and fusing logits scores from the two best-performing fine-tuned models. The system utilizes the Wav2Vec2 front-end feature extractor and the AASIST back-end classifier as the baseline model. During model fine-tuning, three distinct DA policies have been investigated: single-DA, random-DA, and cascade-DA. Moreover, the employed GAM-based co-enhancement strategy, designed to fine-tune the augmented model at both data and optimizer levels, helps the Adam optimizer find flatter minima, thereby boosting model generalization. Overall, the final fusion system achieves a minDCF of 0.115 and an EER of 4.04% on the evaluation set.<|reference_end|>
@article{xu2024szu-afs,
    title={SZU-AFS Antispoofing System for the ASVspoof 5 Challenge},
    author={Yuxiong Xu, Jiafeng Zhong, Sengui Zheng, Zefeng Liu, and Bin Li},
    journal={arXiv preprint arXiv:2408.09933},
    year={2024},
    archivePrefix={arXiv},
    eprint={2408.09933},
    primaryClass={cs.SD cs.AI eess.AS}
}