Relative Phase Shift Features for Replay Spoof Detection System

Abstract

The replay spoofing tries to fool the Automatic Speaker Verification (ASV) system by the recordings of a genuine utterance. Most of the studies have used magnitude-based features and ignored phase-based features for replay detection. However, the phase-based features also affected due to the environmental characteristics during recording. Hence, the phase-based features, such as parameterized Relative Phase Shift (RPS) and Modified Group Delay are used in this paper along with the baseline feature set, namely, Constant Q Cepstral Coefficients (CQCC) and Mel Frequency Cepstral Coefficients (MFCC). We found out that the score-level fusion of magnitude and phase-based features are giving better performance than the individual feature sets alone on the ASV Spoof 2017 Challenge version 2. In particular, the Equal Error Rate (EER) is 12.58 % on the evaluation set with the fusion of RPS and the CQCC feature sets using Gaussian Mixture Model (GMM) classifier.