Liu, M., Wang, L., Lee, K. A., Chen, X., Dang, J. (2021). Replay-Attack Detection Using Features With Adaptive Spectro-Temporal Resolution. ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). doi:10.1109/icassp39728.2021.9414250
Abstract:
Variable-resolution processing aims to improve the feature representation ability by enlarging the local discriminative details. In previous anti-spoofing studies, different phones and frequency regions were both proven to have various levels of sensitivity to replay distortion. In this paper, an adaptive spectro-temporal resolution is proposed to obtain the optimal scale in the feature space: the frequency resolution is adaptive to frequency discrimination, while the temporal resolution is adaptive to continuous phones. In the process, phone-frequency F-ratio analysis is applied to investigate the sensitivity divergences to replay distortion among phones and frequencies. Then, attentive filters are designed to automatically adapt to the phone-frequency discrimination. Validation experiments for the proposed method are conducted on two well-acknowledged magnitude and phase features. A comparative analysis on the ASVspoof 2017 V2.0 database demonstrates that our proposed adaptive spectro-temporal resolution method attains considerably higher error reduction rates than the approaches involving the corresponding original resolution features.
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Funding Info:
This research / project is supported by the The National Key R&D Program of China - -
Grant Reference no. : 2018YFB1305200
This research / project is supported by the National Natural Science Foundation of China - -
Grant Reference no. : 61771333
This research / project is supported by the Tianjin Municipal Science and Technology Project - -
Grant Reference no. : 18ZXZNGX00330