Speaker Verification Anti-Spoofing Using Linear Prediction Residual Phase Features

Abstract

The vulnerability of automatic speaker verification (ASV) systems against spoofing attacks is an important security concern about the reliability of ASV technology. Recently, various countermeasures have been developed for spoofing detection. In this paper, we propose to use features derived from linear prediction (LP) residual signal for spoofing detection using simple Gaussian mixture model (GMM) classifier. Experiments conducted on recently released ASVspoof 2015 database show that LP residual phase cepstral coefficients (LPRPC) outperforms standard MFCC features and considerably improves the spoofing detection performance. With the LPRPC features 97% relative improvement is observed over standard MFCC features on known attacks.