End-to-End Spectro-Temporal Graph Attention Networks for Speaker Verification Anti-Spoofing and Speech Deepfake Detection

Understand

Artefacts that serve to distinguish bona fide speech from spoofed or deepfake speech are known to reside in specific subbands and temporal segments.

• Various approaches can be used to capture and model such artefacts, however, none works well across a spectrum of diverse spoofing attacks.
• Reliable detection then often depends upon the fusion of multiple detection systems, each tuned to detect different forms of attack.
• In this paper we show that better performance can be achieved when the fusion is performed within the model itself and when the representation is learned automatically from raw waveform inputs.