Novel Enhanced Teager Energy Based Cepstral Coefficients for Replay Spoof Detection

Abstract

Replay attack on voice biometric, refers to the fraudulent attempt made by an imposter to spoof another person's identity by replaying the pre-recorded voice samples in front of an Automatic Speaker Verification (ASV) system. In an attempt to develop countermeasures against replay attack, this paper proposes to use a new feature set, namely, Enhanced Teager Energy Cepstral Coefficients (ETECC) using the recently introduced concept of signal mass. Results obtained on ASVspoof 2017 version 2.0 dataset suggest that the proposed feature set performs better than the original Teager Energy Cepstral Coefficients (TECC) feature set because the Enhanced Teager Energy Operator (ETEO) gives a better estimate of signal's energy as compared to the Teager Energy Operator (TEO). We obtained 53.3% and 51.35% reduction in EER on development and evaluation dataset, respectively, with respect to the baseline system.