SUT System Description for Anti-Spoofing 2017 Challenge

Reliability of Automatic Speaker Verification (ASV) systems has always been a concern in dealing with spoofing attacks. Among these attacks, replay attack is the simplest and the easiest accessible method. This paper describes a replay spoofing detection system applied to ASVspoof2017 corpus. To reach this goal, features such as Constant-Q Cepstral Coefficients (CQCC), Modified Group Delay (MGD), Mel Frequency Cepstral Coefficients (MFCC), Relative Spectral Perceptual Linear Predictive (RASTA-PLP) and Linear Prediction Cepstral Coefficients (LPCC), and different classifiers including Gaussian Mixture Models (GMM), MultiLayer Perceptron (MLP), Support Vector Machine (SVM) and Linear Gaussian (LG) classifier have been employed. We also used identity vector (i-vector) based utterance representation. Finally, scores of different subsystems have been fused to construct the proposed system. The results show that the best performance is attained using this score level fusion.