优化语音反欺骗模型，提速降参还更稳。
SpAArSIST: Sparsified AASIST for Efficient and Reliable Anti-Spoofing
- 用可训练的池化策略替换原复杂结构，分训/推理阶段独立控制节点数量。
- 计算量减少20.7%，参数量减少4.1%，跨域测试错误率降至2.82%。
- 提供综合评分工具，帮助选择性能与效率平衡的部署模型。
我们提出SpAArSIST，对广泛使用的基于自监督学习的AASIST语音反欺骗模型进行面向部署的优化。针对公开实现中存在的冗余操作，将学习型池化和堆栈节点注意力替换为显式的轻量级设计：分离训练与推理阶段的图池化比例（k_tr, k_inf），基于幅度的节点评分，以及节点均值聚合。最佳配置（排名1）使后端计算量降低20.7%（从195.045M降至154.706M MACs），模型参数减少4.1%（从611.8k降至586.4k），同时在In-the-Wild数据集上出域鲁棒性提升至EER 2.82%、minDCF 0.078（原为4.64%和0.133），且在ASVspoof5上仍保持竞争力。我们进一步提供一个融合准确率、校准性和计算开销的综合评分，支持部署导向的模型选择。
原文摘要 · Abstract (English)
We present SpAArSIST, a deployment-oriented refinement of the widely used AASIST graph pooling backend for self-supervised learning (SSL) based anti-spoofing. Motivated by redundant operations in public implementations, we replace learned pooling and stack-node attention with explicit, lightweight choices: separate train and inference graph pooling ratios $(k_{\mathrm{tr}},k_{\mathrm{inf}})$, magnitude-based node scoring, and mean aggregation of graph nodes. The best overall configuration (rank 1) cuts backend compute by 20.7% (195.045M $\rightarrow$ 154.706M MACs) and model size by 4.1% (611.8k $\rightarrow$ 586.4k params), while improving out-of-domain robustness on In-the-Wild to 2.82% EER and 0.078 minDCF (from 4.64% and 0.133) and remaining competitive on ASVspoof5. We further provide a composite selection score that summarizes accuracy, calibration, and compute to support balanced deployment-oriented model choice.
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