Graph attention networks for anti-spoofing

Tak, Hemlata; Jung, Jee-weon; Patino, José; Todisco, Massimiliano; Evans, Nicholas
Submitted to INTERSPEECH 2021, Conference of the International Speech Communication Association, 30 August-3 September 2021, Brno, Czechia (Virtual Conference)

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.

 
 

Type:
Conference
Date:
2021-04-07
Department:
Digital Security
Eurecom Ref:
6523
Copyright:
© ISCA. Personal use of this material is permitted. The definitive version of this paper was published in Submitted to INTERSPEECH 2021, Conference of the International Speech Communication Association, 30 August-3 September 2021, Brno, Czechia (Virtual Conference) and is available at :

PERMALINK : https://www.eurecom.fr/publication/6523