Modern automatic speaker verification (ASV) systems are highly vulnerable to spoof attacks and developing ASV anti-spoofing algorithms to protect ASV systems form these attacks is currently a part of active research. Contrarily to current trends on development of stand-alone spoof detection system, this work aims detection of replay attacks directly on the ASV system. The claim made through replay spoofing trials is rejected as impostors directly by ASV system. The objective here is to model the changes in the excitation signal characteristics caused by playback devices for replay detection. Accordingly, two linear prediction (LP) residual based source features are proposed for rejecting replay spoofing trials namely, RMFCC (residual mel-frequency cepstral coefficients) and LPRHEMFCC (LP residual Hilbert envelope MFCC). A comparative analysis between these two source features has been performed through speaker verification experiments to evaluate their effectiveness for ASV anti-spoofing applications. The comparison between the two has been made in the form of (source feature + MFCC) combination. The experiments are conducted using self-developed IITG-MV replay database. From the experimental results, it has been observed that 'LPRHEMFCC+MFCC' combination outperforms 'RMFCC+MFCC' combination, under replay attacks. Finally, the experiments are repeated on ASVspoof2017 database to validate the efficacy of proposed work.
Cite as: Singh, M., Pati, D. (2018) Linear Prediction Residual based Short-term Cepstral Features for Replay Attacks Detection. Proc. Interspeech 2018, 751-755, doi: 10.21437/Interspeech.2018-1128
@inproceedings{singh18_interspeech,
author={Madhusudan Singh and Debadatta Pati},
title={{Linear Prediction Residual based Short-term Cepstral Features for Replay Attacks Detection}},
year=2018,
booktitle={Proc. Interspeech 2018},
pages={751--755},
doi={10.21437/Interspeech.2018-1128}
}