New replay attack detection using iterative adaptive inverse filtering and high frequency band

Abstract

Replay attack is most vulnerable to automatic speaker verification system, where the frauds get the access by replaying pre-recorded voice samples of the genuine speakers. In this proposed work, we mainly concentrated on glottal excitation and high frequency band. First, we demonstrate the importance of the glottal information of the speech signals to detect the replay attack for speaker verification system along with magnitude based discrimination power features set. Iterative Adaptive Inverse Filtering (IA-IF) technique is used to extract the glottal excitation information of the given speech spectrum which shows the difference in the characteristics of genuine and replayed speech sample. Using this technique the glottal information is gained by eliminating the lip radiation and vocal tract effect by applying the integration and inverse filtering respectively. Secondly, we have shown the prominence and discriminative information to detect the replayed attack which is present in high frequency band of the speech spectrums of genuine and replayed samples. Finally, Constant-Q Cepstral Coefficients (CQCC) is used to extract desired features set from the high-frequency bands of glottal excitation spectrum. From the experimental studies done on ASV spoof-2017 version 2.0 database it shows that the proposed method feature set significantly decreases the Equal Error Rate (EER) to 3.68% and 8.32% for development and evaluation set when compare to other state-of-art method feature set.