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Detection of replay spoof speech using teager energy feature cues

机译：使用Teager Energy功能提示检测重播恶搞语音

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摘要

The vulnerability of Automatic Speaker Verification (ASV) systems to spoofing or presentation attacks is still an open security issue. In this context, replay spoofing attacks pose a great threat to an ASV system since they can be easily performed (using a playback device, and without needing any technical skill). In this paper, we analyze replay speech signals in terms of reverberation that may occur during recording of the speech signal. Such reverberation introduces delay and changes in amplitude, producing close copies of speech signals, which significantly influences the replay components. To that effect, we propose to exploit the capabilities of the Teager Energy Operator (TEO) to compute a running estimate of subband energies for replay vs. genuine signals. We have used a linearly-spaced Gabor filterbank to obtain a narrowband filtered signal. The TEO has the ability to track the instantaneous changes of a signal. Experiments are performed on the ASVspoof 2017 Challenge version 2.0 database using a Gaussian Mixture Model (GMM) as pattern classifier. Furthermore, we compared our results with state-of-the-art feature sets, namely, Constant Q Cepstral Coefficients (CQCC), Linear Frequency Cepstral Coefficients (LFCC), Mel Frequency Cepstral Coefficients (MFCC), and used their score-level fusion with the proposed feature sets, i.e., Teager Energy Cepstral Coefficients (TECC), in order to obtain possible complementary information that further reduces the Equal Error Rate (EER). Relatively low EERs are obtained with score-level fusion of CQCC, MFCC, LFCC, and TECC feature sets, resulted in 6.68% and 10.45% on development and evaluation sets, respectively. Moreover, for the evaluation dataset, we also studied the performance of the TECC feature set on different Replay Configurations (RC), namely, for acoustic environments, playback, and recording devices. For all the levels of threat conditions (i.e., low, medium, and high-level) to an ASV system, the proposed feature set performed better compared to existing state-of-the-art feature sets. In addition to the ASVspoof 2017 Challenge database, we also performed experiments on other spoofing databases, namely, the ASVspoof 2015 Challenge, BTAS 2016, and ASVspoof 2019 Challenge databases. For all the spoofing databases used in this study, the proposed TECC feature set perform significantly better than the other feature sets.
机译：自动扬声器验证（ASV）系统对欺骗或演示攻击的脆弱性仍然是一个开放的安全问题。在此上下文中，重放欺骗攻击对ASV系统构成了很大的威胁，因为它们可以轻松地执行（使用播放设备，并且不需要任何技术技能）。在本文中，我们根据在记录语音信号期间可能发生的混响来分析重播语音信号。这种混响引入了幅度和变化的幅度，产生了语音信号的密切副本，这显着影响了重播组件。为此，我们建议利用TEAGET能量运算符（TEO）的能力来计算重放与真正信号的次带能量的运行估计。我们使用了线性间隔的Gabor滤波器堆库来获得窄带滤波信号。 TEO能够跟踪信号的瞬时变化。使用高斯混合模型（GMM）作为图案分类器，对ASVSpof 2017挑战2.0数据库进行实验。此外，我们将结果与最先进的特征集进行了比较，即常数Q谱系齐系数（CQCC），线性频率谱系数（LFCC），MEL频率谱系数（MFCC），并使用了它们的得分级融合利用所提出的特征集，即Teager能量谱系数（TECC），以获得进一步降低相同误差率（eer）的可能的互补信息。通过CQCC，MFCC，LFCC和TECC特征集的得分水平融合获得比较低的EERS，分别导致开发和评估集的6.68％和10.45％。此外，对于评估数据集，我们还研究了在不同重放配置（RC）上设置的TECC功能的性能，即用于声学环境，播放和录制设备。对于ASV系统的所有威胁条件（即，低，中等和高级）的所有级别，与现有的最先进的功能集相比，所提出的特征集更好地执行。除了ASVSpoof 2017挑战数据库之外，我们还在其他欺骗数据库进行了实验，即AsvspoOf 2015挑战，BTAS 2016和AsvSpoot 2019挑战数据库。对于本研究中使用的所有欺骗数据库，所提出的TECC功能集可比其他特征集更好。

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  • 来源
    《Computer speech and language》 |2021年第1期|101140.1-101140.19|共19页
  • 作者

    Madhu R. Ramble; Hemant A. Patil;

  • 作者单位

    Speech Research Lab Dhirubhai Arabani Institute of Information and Communication Technology (DA-1ICT) Gandhinagar-382007 Gujarat India;

    Speech Research Lab Dhirubhai Arabani Institute of Information and Communication Technology (DA-1ICT) Gandhinagar-382007 Gujarat India;

  • 收录信息 美国《科学引文索引》(SCI);美国《工程索引》(EI);
  • 原文格式 PDF
  • 正文语种 eng
  • 中图分类
  • 关键词

    Automatic speaker verification; Spoof; ReplayReverberation; TEO profile;

    机译：自动扬声器验证;欺骗;重新翻译reverberation;Teo简介;

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