Year: 2025
Type: article
Abstract: 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.
Source: Symmetry
DOI: 10.3390/sym17050641
Authors: Yun Tan, Xiaoqian Weng, Jiangzhang Zhu
Institution: Central South University of Forestry and Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $2,896
Award: 基于跨模态语义信息的生成式文本隐写研究
OpenAlex ID: W4409771463