Graph Attention Networks for Anti-Spoofing

TL;DR

This paper introduces a graph attention network approach to model relationships between spectral sub-bands and temporal segments for improved anti-spoofing detection in speaker verification, outperforming baseline models.

Contribution

It applies graph attention networks to model relationships in spectral and temporal data, enhancing anti-spoofing detection performance over existing methods.

Findings

GAT-based models outperform baseline systems.

Fusion of GAT with conventional methods yields 47% performance improvement.

GAT models are complementary to existing anti-spoofing systems.

Abstract

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.