Spoofing-Aware Speaker Verification Robust Against Domain and Channel Mismatches

TL;DR

This paper introduces an integrated, meta-learning-based speaker verification framework that simultaneously addresses spoofing, domain, and channel mismatches, significantly improving robustness in complex real-world scenarios.

Contribution

It presents a novel asymmetric dual-path, multi-task learning approach with spoofing attack simulation within a meta-learning paradigm for comprehensive robustness.

Findings

Significant performance improvements over traditional ASV systems.

Effective handling of multiple threats simultaneously.

Robust generalization across different conditions.

Abstract

In real-world applications, it is challenging to build a speaker verification system that is simultaneously robust against common threats, including spoofing attacks, channel mismatch, and domain mismatch. Traditional automatic speaker verification (ASV) systems often tackle these issues separately, leading to suboptimal performance when faced with simultaneous challenges. In this paper, we propose an integrated framework that incorporates pair-wise learning and spoofing attack simulation into the meta-learning paradigm to enhance robustness against these multifaceted threats. This novel approach employs an asymmetric dual-path model and a multi-task learning strategy to handle ASV, anti-spoofing, and spoofing-aware ASV tasks concurrently. A new testing dataset, CNComplex, is introduced to evaluate system performance under these combined threats. Experimental results demonstrate that our integrated model significantly improves performance over traditional ASV systems across various scenarios, showcasing its potential for real-world deployment. Additionally, the proposed framework's ability to generalize across different conditions highlights its robustness and reliability, making it a promising solution for practical ASV applications.