Tackling Spoofing-Aware Speaker Verification with Multi-Model Fusion

TL;DR

This paper introduces a multi-model fusion framework for spoofing-aware speaker verification that significantly enhances robustness against spoofing attacks, achieving an 86% relative reduction in error rate.

Contribution

It proposes a novel fusion-based SASV system combining multiple state-of-the-art models, substantially improving spoofing detection and speaker verification performance.

Findings

SASV-EER reduced from 8.75% to 1.17%.

Achieved 86% relative improvement over baseline.

Demonstrated effectiveness of multi-model fusion in SASV.

Abstract

Recent years have witnessed the extraordinary development of automatic speaker verification (ASV). However, previous works show that state-of-the-art ASV models are seriously vulnerable to voice spoofing attacks, and the recently proposed high-performance spoofing countermeasure (CM) models only focus solely on the standalone anti-spoofing tasks, and ignore the subsequent speaker verification process. How to integrate the CM and ASV together remains an open question. A spoofing aware speaker verification (SASV) challenge has recently taken place with the argument that better performance can be delivered when both CM and ASV subsystems are optimized jointly. Under the challenge's scenario, the integrated systems proposed by the participants are required to reject both impostor speakers and spoofing attacks from target speakers, which intuitively and effectively matches the expectation of a reliable, spoofing-robust ASV system. This work focuses on fusion-based SASV solutions and proposes a multi-model fusion framework to leverage the power of multiple state-of-the-art ASV and CM models. The proposed framework vastly improves the SASV-EER from 8.75% to 1.17\%, which is 86% relative improvement compared to the best baseline system in the SASV challenge.