AGDT: Audio Deepfake Detection via Asymmetric Gated Dual-Stage Temporal Modeling

In audio deepfake detection under unseen generators, channel distortions, and real-world recording conditions, discriminative evidence may be distributed across representation depths, temporal scales, and utterance-level statistics. To address these characteristics, we propose AGDT, a framework that aggregates evidence across layer depth, temporal scale, and distributional statistics, using XLS-R as its frontend. First, non-competitive sensitive layer gating is applied to the frame-level representations of the XLS-R Transformer layers, allowing multiple layers with complementary discriminative information to contribute to feature fusion. Next, a multi-scale temporal convolution module applies sequential channel and temporal gates to local residual updates before a Conformer backend, while a second module refines the contextual representations without additional gating. This asymmetric configuration places selective local processing before the backend encoder and ungated refinement after it. Finally, attentive statistics pooling combines weighted means and standard deviations to capture both the central tendency and temporal dispersion of frame-level features. For the downstream detection task, the model is trained only on the ASVspoof 2019 LA training set and evaluated on multiple benchmarks involving shifts across datasets, channels, and languages. It achieves equal error rates of 2.48%, 1.56%, 13.57%, and 5.83% on ASVspoof 2021 LA, ASVspoof 2021 DF, ASVspoof 5, and In-the-Wild, respectively. The experimental results demonstrate competitive performance across multiple evaluation domains.