Replay attacks pose the most severe threat to automatic speaker verification systems among various spoofing attacks. In this paper, we propose a novel feature extraction method that leverages both the phase-based and magnitude-based features. The proposed method fully utilizes the subband information and the complementary information from the phase and magnitude spectra. First, we conduct a discriminative performance analysis on full frequency bands via the F-ratio method. Then, variable-frequency resolution features are extracted via several techniques to capture highly discriminative information on frequency bands. Finally, complementary information from the phase and magnitude domains are fused to achieve higher performance. The results on the ASVspoof 2017 database demonstrate that our proposed frequency adaptive features attain relative error reduction rates of 83.4% and 62.3% on the development and evaluation datasets, respectively, compared to the baseline method.

在各种欺骗攻击中，重播攻击对自动扬声器验证系统构成了最严重的威胁。在本文中，我们提出了一种新颖的特征提取方法，该方法同时利用了基于相位和基于幅度的特征。所提出的方法充分利用了来自相位和幅度谱的子带信息和互补信息。首先，我们通过F比率方法对全频带进行判别性能分析。然后，通过几种技术提取可变频率分辨率特征，以捕获频带上的高度区分性信息。最后，融合了来自相位和幅度域的补充信息，以实现更高的性能。