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首页> 外文会议>IEEE International Conference on Acoustics, Speech and Signal Processing >Analysis of Reverberation via Teager Energy Features for Replay Spoof Speech Detection
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Analysis of Reverberation via Teager Energy Features for Replay Spoof Speech Detection

机译：通过Teager能量功能进行混响分析，以重放欺骗性语音检测

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The Automatic Speaker Verification (ASV) systems are vulnerable to spoofing attacks. Detecting replay attack is the challenging Spoof Speech Detection (SSD) task, as several factors are involved during replay mechanism. Hence, it is important to analyze these factors for effective SSD task. This paper introduces the analysis of the replay speech focusing only on the effect of reverberation on the replay speech. The reverberation introduces delay and change in amplitude producing close copies of natural signal that makes natural components inseparable from the replay components and hence, fails to classify the replay speech signal. To that effect, we propose use of Teager Energy Operator (TEO) to compute running estimate of subband energies for replay vs. natural signal. These subband energies are mapped to cepstraldomain to get proposed Teager Energy Cepstral Coefficients (TECC) for replay SSD task. With the TECC feature set, we analyzed the individual performance for all the Relay Configurations (RC) with Gaussian Mixture Model (GMM) as classifier. The experimental results gave lower Equal Error Rate (EER) of 11.73 % with TECC features and further reduced to 10.30 % with score-level fusion of LFCC and TECC features on evaluation dataset of ASVspoof 2017 challenge version 2.0 database.
机译：自动扬声器验证（ASV）系统容易受到欺骗攻击。检测重播攻击是具有挑战性的欺骗性语音检测（SSD）任务，因为重播机制涉及多个因素。因此，分析这些因素对于有效执行SSD任务很重要。本文仅针对混响对重播语音的影响，介绍重播语音的分析。混响会引入延迟和幅度变化，从而产生自然信号的近似副本，从而使自然分量与重播分量不可分离，因此无法对重播语音信号进行分类。为此，我们建议使用Teager能量算子（TEO）计算重播与自然信号的子带能量的运行估计。将这些子带能量映射到倒谱域，以获得拟议的Teager能量倒谱系数（TECC）用于重放SSD任务。利用TECC功能集，我们以高斯混合模型（GMM）为分类器分析了所有继电器配置（RC）的单独性能。实验结果显示，在ASVspoof 2017挑战版2.0数据库的评估数据集上，具有TECC功能的均等错误率（EER）较低，为11.73％，而通过LFCC和TECC功能的分数级融合进一步降低至10.30％。

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