Features and Classifiers for Replay Spoofing Attack Detection

Abstract

Automatic speaker verification (ASV) systems are known to be highly vulnerable against spoofing attacks. Various successful countermeasures have recently been proposed to detect spoofing attacks originating from speech synthesis (SS) and voice conversion (VC). However, detecting replay attacks, the most easily implementable spoofing attacks against ASV systems, has gained less attention. Thus, in this paper we present an experimental comparison of various feature extraction techniques and classifiers for replay attack detection. In total, six magnitude spectrum and three phase spectrum based features are used for feature extraction. For classification in turn, four different techniques are utilized. Experiments are conducted on recently released ASVspoof 2017 replay attack detection challenge. Experimental results reveals that magnitude spectrum features considerably outperform phase based features independent of the classifier. Comparative results using four different classifiers indicate that i-vector cosine scoring yields lower equal error rates (EERs) than other methods.