ISCA Archive Interspeech 2021
ISCA Archive Interspeech 2021

Graph Attention Networks for Anti-Spoofing

Hemlata Tak, Jee-weon Jung, Jose Patino, Massimiliano Todisco, Nicholas Evans

The cues needed to detect spoofing attacks against automatic speaker verification are often located in specific spectral sub-bands or temporal segments. Previous works show the potential to learn these using either spectral or temporal self-attention mechanisms but not the relationships between neighbouring sub-bands or segments. This paper reports our use of graph attention networks (GATs) to model these relationships and to improve spoofing detection performance. GATs leverage a self-attention mechanism over graph structured data to model the data manifold and the relationships between nodes. Our graph is constructed from representations produced by a ResNet. Nodes in the graph represent information either in specific sub-bands or temporal segments. Experiments performed on the ASVspoof 2019 logical access database show that our GAT-based model with temporal attention outperforms all of our baseline single systems. Furthermore, GAT-based systems are complementary to a set of existing systems. The fusion of GAT-based models with more conventional countermeasures delivers a 47% relative improvement in performance compared to the best performing single GAT system.

doi: 10.21437/Interspeech.2021-993

Cite as: Tak, H., Jung, J.-w., Patino, J., Todisco, M., Evans, N. (2021) Graph Attention Networks for Anti-Spoofing. Proc. Interspeech 2021, 2356-2360, doi: 10.21437/Interspeech.2021-993

  author={Hemlata Tak and Jee-weon Jung and Jose Patino and Massimiliano Todisco and Nicholas Evans},
  title={{Graph Attention Networks for Anti-Spoofing}},
  booktitle={Proc. Interspeech 2021},