Paper ID | SPE-26.6 | ||
Paper Title | Replay-Attack Detection using Features with Adaptive Spectro-Temporal Resolution | ||
Authors | Meng Liu, Longbiao Wang, Tianjin University, China; Kong Aik Lee, Agency for Science, Technology and Research (A*STAR), Singapore; Xuanda Chen, Chinese Academy of Social Sciences, China; Jianwu Dang, Japan Advanced Institute of Science and Technology, Japan | ||
Session | SPE-26: Speaker Verification Spoofing and Countermeasures | ||
Location | Gather.Town | ||
Session Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
Presentation Time: | Wednesday, 09 June, 15:30 - 16:15 | ||
Presentation | Poster | ||
Topic | Speech Processing: [SPE-SPKR] Speaker Recognition and Characterization | ||
IEEE Xplore Open Preview | Click here to view in IEEE Xplore | ||
Abstract | Variable-resolution processing aims to improve the feature representation ability by enlarging the local discriminative details. In previous anti-spoofing studies, phones and frequencies were both proven to be sensitive 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. |