This paper presents our contribution to the ASVspoof 2017 Challenge. It addresses a replay spoofing attack against a speaker recognition system by detecting that the analysed signal has passed through multiple analogue-to-digital (AD) conversions. Specifically, we show that most of the cues that enable to detect the replay attacks can be found in the high-frequency band of the replayed recordings. The described anti-spoofing countermeasures are based on (1) modelling the subband spectrum and (2) using the proposed features derived from the linear prediction (LP) analysis. The results of the investigated methods show a significant improvement in comparison to the baseline system of the ASVspoof 2017 Challenge. A relative equal error rate (EER) reduction by 70% was achieved for the development set and a reduction by 30% was obtained for the evaluation set.
Cite as: Witkowski, M., Kacprzak, S., Żelasko, P., Kowalczyk, K., Gałka, J. (2017) Audio Replay Attack Detection Using High-Frequency Features. Proc. Interspeech 2017, 27-31, doi: 10.21437/Interspeech.2017-776
@inproceedings{witkowski17_interspeech,
author={Marcin Witkowski and Stanisław Kacprzak and Piotr Żelasko and Konrad Kowalczyk and Jakub Gałka},
title={{Audio Replay Attack Detection Using High-Frequency Features}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={27--31},
doi={10.21437/Interspeech.2017-776}
}