Effectiveness of different Spectral Features in Replay Attack Detection - An Experimental Study

The Spoofing attacks poses serious threats to Automatic Speaker verification (ASV) systems. Replay attack is one of such spoofing attacks which is a low effort yet high-risk attack due to ease of availability as well as widespread usage of smart phones, recording and playback devices. In replay spoofing attack, the attacker replays a pre-recorded speech samples of a genuine speaker to get unauthorized access. This study is focused on experimental analysis of different features with Gaussian Mixture Model (GMM) for development of countermeasures system for detection of replay spoofing attacks on ASV systems. GMM is a standard widely used as well as effective classifier for baseline countermeasures in ASV systems. The results are compared based on Equal Error Rate (EER) metric. This work presents a comparison of various spectral features for the most common spoofing attack i.e. replay spoofing attack detection based on its performance over the released ASVspoof challenge dataset of 2017, which is a benchmark dataset dedicated to address replay spoofing attacks only