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arXiv · 2607.20023

Layer-Wise Decision Fusion for Fake Audio Detection Using XLS-R

Yixuan Xiao·Ngoc Thang Vu
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Abstract

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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BibTeXRIS

Yixuan Xiao, Ngoc Thang Vu. 2026-07-22. Layer-Wise Decision Fusion for Fake Audio Detection Using XLS-R. https://doi.org/10.21437/interspeech.2025-1543

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