针对环境音与语音分别篡改的新型欺骗检测难题，提出三阶段级联框架。
EnvTriCascade: An Environment-Aware Tri-Stage Cascaded Framework for ESDD2 2026 Challenge
- 三阶段级联设计：先判混合真伪，再用双分支特征融合检测篡改类型。
- 在CompSpoofV2数据集上达到0.8266的宏平均F1，排名挑战赛第二。
- 适合研究音频安全、对抗复杂伪造场景的工程师和研究人员。
真实场景中的语音欺骗已从仅语音伪造演变为更复杂的组件级设置，其中语音与环境声音可独立被操纵。为应对这一挑战，本文提出针对ESDD2挑战赛的环境感知三阶段级联框架EnvTriCascade。首先，混合一致性检测器提供二元先验，用于区分原始录音与篡改混合信号，以校准最终决策。其次，两个互补的五分类检测器分别利用SSLAM+XLS-R与EAT-large+XLS-R的表征，通过跨分支注意力门控分类器整合多分支特征。为增强对多样混合条件的鲁棒性，引入RawBoost数据增强。系统仅在官方CompSpoofV2数据集上训练，测试集上取得0.8266的宏平均F1分数，显著优于官方基线，位列挑战赛第二名。
原文摘要 · Abstract (English)
ADD in real-world scenarios has evolved from speech-only spoofing to more challenging component-level settings, where speech and environmental sounds may be independently manipulated. To tackle this, we propose EnvTriCascade, an Environment-Aware Tri-Stage Cascaded framework for the ESDD2 Challenge. First, a mix-consistency detector provides a binary prior to distinguish original recordings from manipulated mixtures, which calibrates the final decisions. Next, two complementary five-class detectors, leveraging SSLAM+XLS-R and EAT-large+XLS-R representations, extract robust multi-branch features integrated via a cross-branch attention-gated classifier. To enhance robustness against diverse mixing conditions, we incorporate RawBoost augmentation. Trained exclusively on the official CompSpoofV2 dataset, our system achieves a Macro-F1 score of 0.8266 on the test set, significantly outperforming the official baseline and ranking second in the challenge.
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