Increasing anti-spoofing protection in speaker verification using linear prediction

Abstract

This article addresses the problem of anti-spoofing protection in an automatic speaker verification (ASV) system. An improved version of a previously proposed spoofing countermeasure is presented. The presented method is based on the analysis of linear prediction error that results from both short-and long-term prediction of the input speech signal. It was observed that non-natural speech signals, i.e., synthetic or converted speech, were predicted in a different way than genuine speech. Therefore, in contrast to the classical linear prediction analysis, where usually only the prediction coefficients are analyzed, in the proposed approach the residual (error) signals were examined. During this analysis, 23 various prediction parameters were extracted, such as the energy of the prediction error, prediction gains and temporal parameters related to the prediction error signals. Various binary classifiers were researched to separate human and spoof classes, however the support vector machines with radial basis function (SVM-RBF) yielded the best results. When tested on the corpora provided for the ASVspoof 2015 Challenge, the proposed countermeasure returned better results than the previous version of the algorithm and, in most of the cases, the baseline spoofing detector based on the local binary patterns (LBP). It is hoped that the proposed method can be part of a generalized spoofing countermeasure helping to increase security of ASV systems.The calculations described in the article were made in the Interdisciplinary Centre for Mathematical and Computational Modelling (ICM) of the University of Warsaw (computational grant No. G46-2).

Audio Replay Attack Detection Using High-Frequency Features

“…Finally, LPCCres features combine both LPCC features concatenated with sub-band cepstrum of a residual. Note that the features based on the residual signal have also been used in the previous anti-spoofing challenge [21,22]. The block diagram of the LPCCres processing is shown in Figure 4.…”