Efficient Attention Branch Network with Combined Loss Function for Automatic Speaker Verification Spoof Detection

TL;DR

This paper introduces the Efficient Attention Branch Network with a combined loss function to improve the generalization of spoof detection in automatic speaker verification systems against unseen attacks.

Contribution

It proposes a novel modular architecture and loss function that enhance robustness and generalization in spoof detection tasks.

Findings

Improved detection accuracy on unseen spoof attacks.

Enhanced robustness compared to existing methods.

Better generalization demonstrated in experiments.

Abstract

Many endeavors have sought to develop countermeasure techniques as enhancements on Automatic Speaker Verification (ASV) systems, in order to make them more robust against spoof attacks. As evidenced by the latest ASVspoof 2019 countermeasure challenge, models currently deployed for the task of ASV are, at their best, devoid of suitable degrees of generalization to unseen attacks. Upon further investigation of the proposed methods, it appears that a broader three-tiered view of the proposed systems. comprised of the classifier, feature extraction phase, and model loss function, may to some extent lessen the problem. Accordingly, the present study proposes the Efficient Attention Branch Network (EABN) modular architecture with a combined loss function to address the generalization problem...