Speech Spoofing Detection Based on Graph Attention Networks with Spectral and Temporal Information
Peng Zhang, Yida Chen, Meijuan Li, Hui Zhao, Jianqiang Zhang, Fuqiang Wang, Xiaoming Wu · 2023
Automatic speaker verification (ASV) systems are vulnerable to synthetic speech attacks. Synthetic algorithms usually introduce artifacts in specific sub-bands or time segments. However, under unknown spoofing attacks, it is challenging to choose the right domain for effective detection. In this paper, we propose a speech spoofing detection method based on graph attention networks with spectral and temporal information. First, high-level features of raw audio are extracted using SENet channel attention to enhance the spatial correlation between speech frames. Then, spectral graph and temporal graph are constructed for the high-level features using graph attention networks. Finally, we design a new heterogeneous multi-domain co-graph attention module to process the information from different domains for effective speech spoofing detection. The proposed model was evaluated on the ASVspoof 2019 dataset and obtains a min t-DCF of 0.0264 and an EER of 0.94%, exhibiting competitive performance. Experiments also show its effectiveness when detecting unknown types of attacks.