For practical automatic speaker verification (ASV) systems, replay attack poses a true risk. By replaying a pre-recorded speech signal of the genuine speaker, ASV systems tend to be easily fooled. An effective replay detection method is therefore highly desirable. In this study, we investigate a major difficulty in replay detection: the over-fitting problem caused by variability factors in speech signal. An F-ratio probing tool is proposed and three variability factors are investigated using this tool: speaker identity, speech content and playback & recording device. The analysis shows that device is the most influential factor that contributes the highest over-fitting risk. A frequency warping approach is studied to alleviate the over-fitting problem, as verified on the ASV-spoof 2017 database.
Cite as: Li, L., Chen, Y., Wang, D., Zheng, T.F. (2017) A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification. Proc. Interspeech 2017, 92-96, doi: 10.21437/Interspeech.2017-456
@inproceedings{li17b_interspeech,
author={Lantian Li and Yixiang Chen and Dong Wang and Thomas Fang Zheng},
title={{A Study on Replay Attack and Anti-Spoofing for Automatic Speaker Verification}},
year=2017,
booktitle={Proc. Interspeech 2017},
pages={92--96},
doi={10.21437/Interspeech.2017-456}
}