Dual-Channel Spoofed Speech Detection Based on Graph Attention Networks

In the field of voice cryptography, detecting forged speech is crucial for secure communication and identity authentication. While most existing spoof detection methods rely on monaural audio, the characteristics of dual-channel signals remain underexplored. To address this, we propose a symmetrical dual-branch detection framework that integrates Res2Net with coordinate attention (Res2NetCA) and a dual-channel heterogeneous graph fusion module (DHGFM). The proposed architecture encodes left and right vocal tract signals into spectrogram and time-domain graphs, and it models both intra- and inter-channel time–frequency dependencies through graph attention mechanisms and fusion strategies. Experimental results on the ASVspoof2019 and ASVspoof2021 LA datasets demonstrate the superior detection performance of our method. Specifically, it achieved an EER of 1.64% and a Min-tDCF of 0.051 on ASVspoof2019, and an EER of 6.76% with a Min-tDCF of 0.3638 on ASVspoof2021, validating the effectiveness and potential of dual-channel modeling in spoofed speech detection.