Feature detection based on linear prediction residual for spoofing countermeasures of speaker verification system

Abstract

The pre-research shows that Linear prediction (LP) residual contains more discriminative information related to replay spoofing attacks, so this paper proposes three features based on LP residual and IMel filter-banks which closely distributed in the high-frequency regions for replay spoofing countermeasures. They are residual IMel frequency cepstral coefficient (RIMFC), LP residual Hilbert envelope IMel frequency cepstral coefficient (LHIMFC) and residual phase cepstral coefficient (RPC). The effectiveness of these features is demonstrated on ASVspoofing2017 Challenge Version 2.0 dataset. Experimental results indicate that the proposed features outperform the baseline system using constant Q cepstral coefficient (CQCC), and the equal error rate (EER) is reduced under the same conditions. Moreover, feature fusions help to achieve higher performance than traditional IMel frequency cepstral coefficient (IMFCC) and CQCC, which indicates that the complementary information of different features is beneficial for detecting replay attacks.