Exploiting Magnitude and Phase Aware Deep Neural Network for Replay Attack Detection

Abstract

Magnitude and phase aware deep neural network (MP aware DNN) based on Fast Fourier Transform information, has recently been received more attention to many speech applications. However, little attention has been paid to its aspect in terms of replay attack detection developed for the automatic speaker verification and countermeasures (ASVspoof 2017). This paper aims to investigate the MP aware DNN as a speech classification for detecting non-replayed (genuine) and replayed speech. Also, to exploit the advantage of the classifier-based complementary to improve the reliable detection decision, we propose a novel method by combining MP aware DNN with standard replay attack detection (that is, the use of constant Q transform cepstral coefficients-based Gaussian mixture model classification: CQCC-based GMM). Experiments are evaluated using ASVspoof 2017 and a standard measure of detection performance called equal error rate (EER). The results showed that MP aware DNN -based detection performed conventional DNN method using only the magnitude/phase features. Moreover, we found that score combination of CQCC-based GMM with MP aware DNN achieved additional improvement, indicating that MP aware DNN is very useful, especially when combined with the CQCC-based GMM for replay attack detection.