改进AASIST模型，提升语音伪造检测精度
Towards Scalable AASIST: Refining Graph Attention for Speech Deepfake Detection
- 用标准化多头注意力替代原图注意力模块
- 在ASVspoof5上达到7.6%的等错误率
- 适合关注语音伪造检测实战优化的研究者
语音转换和文本转语音技术的进步使自动说话人验证（ASV）系统更易受到欺骗攻击。本文对AASIST反欺骗架构进行了适度改进：在数据有限情况下使用冻结的Wav2Vec 2.0编码器以保留自监督语音表征；将原始图注意力模块替换为采用异构查询投影的标准多头注意力；并用可训练的上下文感知融合层替代启发式的帧段融合。在ASVspoof 5数据集上的评估显示，该系统达到7.6%的等错误率（EER），优于相同训练条件下重实现的AASIST基线。消融实验表明各项改动均贡献性能提升，说明针对成熟模型进行有针对性调整有助于增强实际场景中的语音伪造检测能力。代码已公开于https://github.com/KORALLLL/AASIST_SCALING。
原文摘要 · Abstract (English)
Advances in voice conversion and text-to-speech synthesis have made automatic speaker verification (ASV) systems more susceptible to spoofing attacks. This work explores modest refinements to the AASIST anti-spoofing architecture. It incorporates a frozen Wav2Vec 2.0 encoder to retain self-supervised speech representations in limited-data settings, substitutes the original graph attention block with a standardized multi-head attention module using heterogeneous query projections, and replaces heuristic frame-segment fusion with a trainable, context-aware integration layer. When evaluated on the ASVspoof 5 corpus, the proposed system reaches a 7.6\% equal error rate (EER), improving on a re-implemented AASIST baseline under the same training conditions. Ablation experiments suggest that each architectural change contributes to the overall performance, indicating that targeted adjustments to established models may help strengthen speech deepfake detection in practical scenarios. The code is publicly available at https://github.com/KORALLLL/AASIST_SCALING.
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