Long Range Acoustic and Deep Features Perspective on ASVspoof 2019

机译：ASVspoof 2019的远程声学和深度特征透视

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To secure automatic speaker verification (ASV) systems from intruders, robust countermeasures for spoofing attack detection are required. The ASVspoof series of challenge provides a shared anti-spoofing task. The recent edition, ASVspoof 2019, focuses on attacks by both synthetic and replay speech that are referred to as logical and physical access attacks, respectively. In the ASVspoof 2019 submission, we considered novel countermeasures based on long range acoustic features, that are unique in many ways as they are derived using octave power spectrum and subbands, as opposed to the commonly used linear power spectrum. During the post-challenge study, we further investigate the use of deep features that enhances the discriminative ability between genuine and spoofed speech. In this paper, we summarize the findings from the perspective of long range acoustic and deep features for spoof detection. We make a comprehensive analysis on the nature of different kinds of spoofing attacks and system development.

机译：为了保护入侵者的自动扬声器验证（ASV）系统，需要采取有效的对策来欺骗攻击检测。 ASVspoof系列挑战提供了一个共享的反欺骗任务。最新版本的ASVspoof 2019专注于合成语音和重放语音的攻击，分别称为逻辑访问攻击和物理访问攻击。在ASVspoof 2019提交的文件中，我们考虑了基于远程声学特征的新颖对策，这些对策在许多方面都是独特的，因为它们是使用倍频程功率谱和子带而不是常用的线性功率谱得出的。在挑战后研究期间，我们将进一步研究深层功能的使用，这些功能可增强真实语音与欺骗性语音之间的区分能力。在本文中，我们从用于欺骗检测的远程声学和深层特征的角度总结了这些发现。我们对各种欺骗攻击的性质和系统开发进行了综合分析。

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《IEEE Automatic Speech Recognition and Understanding Workshop》|2019年|1018-1025|共8页
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Rohan Kumar Das; Jichen Yang; Haizhou Li;
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Feature extraction; Mel frequency cepstral coefficient; Training; Discrete Fourier transforms;

机译：特征提取;梅尔频率倒谱系数;训练;离散傅里叶变换;

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Long Range Acoustic and Deep Features Perspective on ASVspoof 2019

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