AASIST: Audio anti-spoofing using integrated spectro-temporal graph attention networks

Jung, Jee-weon; Heo, Hee-Soo; Tak, Hemlata; Shim, Hye-jin; Chung, Joon Son; Lee, Bong-Jin; Yu, Ha-Jin; Evans, Nicholas
ICASSP 2022, IEEE International Conference on Acoustics, Speech and Signal Processing, 22-27 May 2022, Singapore, Singapore

Artefacts that differentiate spoofed from bona-fide utterances can reside in spectral or temporal domains. Their reliable detection usually depends upon computationally demanding ensemble systems where each subsystem is tuned to some specific artefacts. We seek to develop an efficient, single system that can detect a broad range of different spoofing attacks without score-level ensembles. We propose a novel heterogeneous stacking graph attention layer which models artefacts spanning heterogeneous temporal and spectral domains with a heterogeneous attention mechanism and a stack node. With a new max graph operation that involves a competitive mechanism and an extended readout scheme, our approach, named AASIST, outperforms the current state-of-the-art by 20% relative. Even a lightweight variant, AASIST-L, with only 85K parameters, outperforms all competing systems.


DOI
Type:
Conférence
City:
Singapore
Date:
2022-05-22
Department:
Sécurité numérique
Eurecom Ref:
6696
Copyright:
© 2022 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.

PERMALINK : https://www.eurecom.fr/publication/6696