通过分层决策融合提升假音频检测准确率，跨数据集表现最优。
Layer-Wise Decision Fusion for Fake Audio Detection Using XLS-R
- 先分层决策再融合，避免特征坍塌
- 在In-the-Wild数据集上误报率仅6.90%
- 模型可解释性强，便于分析决策机制
近期假音频检测方法常利用大型语音模型获取鲁棒的语音表征。这些模型通常深度很深，提供多层表征。然而，现有方法多依赖单一层次表征或特征融合，生成全局表征进行决策，容易忽略多层丰富信息，可能导致特征坍塌。本文提出一种新型分层决策融合方法，在每层独立决策后进行融合，相比其他强基线，在In-the-Wild数据集上达到最低的等错误率（EER 6.90%），实现最佳跨数据集性能。该模型设计还增强了可解释性，使我们能够深入分析决策背后的机制。
原文摘要 · Abstract (English)
Recent fake audio detection methods often leverage large speech models to achieve robust speech representations. These models are typically very deep, providing multiple layer-wise representations. However, current works often rely solely on single layer representation or feature fusion to extract one utterance-level representation for decision making. These methods risk underutilizing rich information from multiple layers and might induce feature collapse. We propose a novel layer-wise decision fusion method that applies fusion after per-layer decision making and achieves the best cross-dataset performance on In-the-Wild dataset (EER 6.90%) compared to other strong baselines. Our model design also makes the model more transparent, allowing us to conduct detailed analysis to reveal the underlying mechanism of decision making.
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