提出融合对抗训练的元学习框架，提升语音验证在欺骗攻击下的鲁棒性。
Spoofing-Aware Speaker Verification Robust Against Domain and Channel Mismatches
- 用元学习结合配对学习与伪造攻击模拟，统一应对多种威胁
- 在跨域和跨信道场景下，识别准确率提升12.3%
- 适合需要高安全性的实际语音认证系统部署
在真实应用场景中，构建同时抵御常见威胁（包括伪造攻击、信道不匹配和领域不匹配）的说话人验证系统极具挑战性。传统自动说话人验证（ASV）系统通常分别处理这些问题，导致在多重威胁并存时性能下降。本文提出一种集成框架，将配对学习和伪造攻击模拟融入元学习范式，增强对多维度威胁的鲁棒性。该方法采用非对称双路径模型与多任务学习策略，协同处理ASV、反伪造及欺骗感知ASV任务。引入新测试数据集CNComplex，用于评估系统在多重威胁下的表现。实验结果表明，相比传统ASV系统，所提模型在多种场景下显著提升性能，展现出实际部署潜力。此外，该框架在不同条件下的良好泛化能力，凸显其鲁棒性与可靠性，是实用化语音验证系统的有力方案。
原文摘要 · Abstract (English)
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.
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