论文标题
基于多任务学习的欺骗性自动扬声器验证系统
Multi-task Learning Based Spoofing-Robust Automatic Speaker Verification System
论文作者
论文摘要
通过产生人工语音发出的欺骗攻击会严重降低说话者验证系统的性能。最近，已经提出了许多用于检测合成语音的攻击类型的反欺骗对策，以重播演示。虽然有许多关于独立抗泡沫解决方案的有效防御措施，但说话者验证和欺骗检测系统的整合具有明显的好处。在本文中，我们提出了一个基于多任务学习体系结构的多种攻击的欺骗性自动扬声器验证（SR-ASV）系统。这种基于深度学习的模型经过了与话语中的时频表示共同培训，可以同时为这两个任务提供识别决策。与ASVSPOOF 2017和2019 CORPORA上的其他最先进的系统相比，可以在不同的欺骗条件下进行大幅改进。
Spoofing attacks posed by generating artificial speech can severely degrade the performance of a speaker verification system. Recently, many anti-spoofing countermeasures have been proposed for detecting varying types of attacks from synthetic speech to replay presentations. While there are numerous effective defenses reported on standalone anti-spoofing solutions, the integration for speaker verification and spoofing detection systems has obvious benefits. In this paper, we propose a spoofing-robust automatic speaker verification (SR-ASV) system for diverse attacks based on a multi-task learning architecture. This deep learning based model is jointly trained with time-frequency representations from utterances to provide recognition decisions for both tasks simultaneously. Compared with other state-of-the-art systems on the ASVspoof 2017 and 2019 corpora, a substantial improvement of the combined system under different spoofing conditions can be obtained.
