论文标题
Rawnet2
End-to-end anti-spoofing with RawNet2
论文作者
论文摘要
欺骗对策旨在保护自动扬声器验证系统免于使用欺骗性的语音信号操纵其可靠性。尽管最新的ASVSPOOF 2019评估结果表明检测大多数攻击的可能性很大，但有些人继续逃避检测。本文报告了Rawnet2在抗烟道上的第一次应用。 Rawnet2摄入原始音频，并且有可能使用更传统的对策解决方案来学习无法检测到的提示。我们描述了对原始Rawnet2体系结构进行的修改，以便将其应用于抗散热器。对于A17攻击，我们的RAWNET2系统结果是第二好的报告，而Rawnet2和基线对策的融合为完整的ASVSPOOF 2019逻辑访问条件报告了第二好的结果。我们的结果可通过开源软件重现。
Spoofing countermeasures aim to protect automatic speaker verification systems from attempts to manipulate their reliability with the use of spoofed speech signals. While results from the most recent ASVspoof 2019 evaluation show great potential to detect most forms of attack, some continue to evade detection. This paper reports the first application of RawNet2 to anti-spoofing. RawNet2 ingests raw audio and has potential to learn cues that are not detectable using more traditional countermeasure solutions. We describe modifications made to the original RawNet2 architecture so that it can be applied to anti-spoofing. For A17 attacks, our RawNet2 systems results are the second-best reported, while the fusion of RawNet2 and baseline countermeasures gives the second-best results reported for the full ASVspoof 2019 logical access condition. Our results are reproducible with open source software.
