Significance of Teager Energy Operator Phase for Replay Spoof Detection

Abstract

The increased use of voice biometrics for various security applications, motivated authors to investigate different countermeasures for the hazard of spoofing attacks, where the attacker tries to imitate the genuine speaker. The replay is the most accessible spoofing attack. Past studies have ignored phase information for various speech processing applications. In this paper, we explore the excitation source-like feature set, namely, Teager Energy Operator (TEO) phase and its significance in the replay spoof detection task. This feature set is further fused at score-level with magnitude spectrum-based features, such as Constant Q Cepstral Coefficients (CQCC), Mel Frequency Cepstral Coefficients (MFCC), and Linear Frequency Cepstral Coefficients (LFCC). The improvement in the results show that the TEO phase feature set contains the complementary information to the magnitude spectrum-based features. The experiments are performed on the ASV Spoof 2017 Challenge database. The systems are implemented with Gaussian Mixture Model (GMM) as a classifier. Our best system using TEO phase achieves the Equal Error Rate (EER) of 6.57% and 15.39% on the development and evaluation set, respectively.