基于噪声类多任务学习的说话人自动验证重放欺骗检测系统

Hye-jin Shim, Jee-weon Jung, Hee-Soo Heo, Sung-Hyun Yoon, Ha-jin Yu
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引用次数: 20

摘要

在本文中,我们提出了一个重放攻击欺骗检测系统,该系统使用多任务学习噪声类来自动验证说话人。我们将重放攻击产生的噪声定义为重放噪声。我们探索了同时训练深度神经网络用于重播攻击欺骗检测和重播噪声分类的有效性。多任务学习包括对播放设备、录音环境和录音设备的噪声进行分类,以及欺骗检测。这三种噪音类别中的每一种还包括一个真正的类别。在asvspof2017 1.0版本数据集上的实验结果表明,我们提出的系统在评估集上的性能相对提高了30%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Replay Spoofing Detection System for Automatic Speaker Verification Using Multi-Task Learning of Noise Classes
In this paper, we propose a replay attack spoofing detection system for automatic speaker verification using multi-task learning of noise classes. We define the noise that is caused by the replay attack as replay noise. We explore the effectiveness of training a deep neural network simultaneously for replay attack spoofing detection and replay noise classification. The multi-task learning includes classifying the noise of playback devices, recording environments, and recording devices as well as the spoofing detection. Each of the three types of the noise classes also includes a genuine class. The experiment results on the version 1.0 of ASVspoof2017 datasets demonstrate that the performance of our proposed system is improved by 30% relatively on the evaluation set.
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