Analysis of Reverberation via Teager Energy Features for Replay Spoof Speech Detection

Abstract

The Automatic Speaker Verification (ASV) systems are vulnerable to spoofing attacks. Detecting replay attack is the challenging Spoof Speech Detection (SSD) task, as several factors are involved during replay mechanism. Hence, it is important to analyze these factors for effective SSD task. This paper introduces the analysis of the replay speech focusing only on the effect of reverberation on the replay speech. The reverberation introduces delay and change in amplitude producing close copies of natural signal that makes natural components inseparable from the replay components and hence, fails to classify the replay speech signal. To that effect, we propose use of Teager Energy Operator (TEO) to compute running estimate of subband energies for replay vs. natural signal. These subband energies are mapped to cepstraldomain to get proposed Teager Energy Cepstral Coefficients (TECC) for replay SSD task. With the TECC feature set, we analyzed the individual performance for all the Relay Configurations (RC) with Gaussian Mixture Model (GMM) as classifier. The experimental results gave lower Equal Error Rate (EER) of 11.73 % with TECC features and further reduced to 10.30 % with score-level fusion of LFCC and TECC features on evaluation dataset of ASVspoof 2017 challenge version 2.0 database.