Replay attack detection using variable-frequency resolution phase and magnitude features. (March 2021)

• Record Type: Journal Article
• Authors: Liu, Meng; Wang, Longbiao; Dang, Jianwu; Lee, Kong Aik; Nakagawa, Seiichi
• Is Part Of: Computer speech & language. Volume 66(2021)
• Publication Date: 2021-03
• Subjects: Replay attack -- Discriminative information -- Frequency modulation -- Adaptive features; Speech processing systems -- Periodicals; Automatic speech recognition -- Periodicals; Computers -- Periodicals; Linguistics -- Periodicals; Speech-Language Pathology -- Periodicals; Traitement automatique de la parole -- Périodiques; Reconnaissance automatique de la parole -- Périodiques; Automatic speech recognition; Speech processing systems; Electronic journals; Periodicals; 006.454
• DOI: 10.1016/j.csl.2020.101161
• Languages: English
• ISSNs: 0885-2308

Abstract

Highlights: F-ratio method was used for anti-spoofing frequency discriminative analysis. Adaptive frequency resolution features were designed according to frequency discriminative analysis. A new adaptive frequency resolution Relative Phase feature was proposed. Three types of adaptive filters were proposed and compared for pros and cons. Complementary information from phase and magnitude were combined in replay detection. Abstract: 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.