Replay attack detection using variable-frequency resolution phase and magnitude features

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.