Multi-band Spectral Entropy Information for Detection of Replay Attacks

Abstract

Replay attacks have been proven to be a potential threat to practical automatic speaker verification systems. In this work, we explore a novel feature based on spectral entropy for the detection of replay attacks. The spectral entropy is a measure to capture spectral distortions and flatness. It is found that the replay speech carries artifacts in the process of recording and playback. We hypothesize that spectral entropy can be a useful information to capture such artifacts. In this regard, we explore multi-band spectral entropy feature for replay attack detection. The studies are conducted on ASVspoof 2017 Version 2.0 database that deals with replay speech attacks. A baseline system with popular constant-Q cepstral coefficient (CQCC) feature is also developed. Finally, a combined system is proposed with multi-band spectral entropy and CQCC features that outperforms the baseline. The experiments validate the idea of multi-band spectral entropy feature.