Recently there have been wide interests in speaker verification for various applications. Although the reported equal error rate (EER) is relatively low, many evidences show that the present speaker verification technologies can be susceptible to malicious spoofing attacks. Inspired by the great success of deep learning in the automatic speech recognition, deep neural network (DNN) based approaches are developed on the spoofing detection for the first time. In this paper, a novel DNN based robust representation is proposed for the spoofing detection to extract the representative spoofing-vector (s-vector). Then the mahalanobis distance and appropriate normalization methods are investigated to get the best system performance. Using the designed deep learning based strategy, our team obtained an impressive result on spoofing detection task, and achieved the 3rd position in the first spoofing detection challenge evaluation, i.e. ASVspoof 2015 Challenge.
Cite as: Chen, N., Qian, Y., Dinkel, H., Chen, B., Yu, K. (2015) Robust deep feature for spoofing detection — the SJTU system for ASVspoof 2015 challenge. Proc. Interspeech 2015, 2097-2101, doi: 10.21437/Interspeech.2015-474
@inproceedings{chen15j_interspeech,
author={Nanxin Chen and Yanmin Qian and Heinrich Dinkel and Bo Chen and Kai Yu},
title={{Robust deep feature for spoofing detection — the SJTU system for ASVspoof 2015 challenge}},
year=2015,
booktitle={Proc. Interspeech 2015},
pages={2097--2101},
doi={10.21437/Interspeech.2015-474}
}