In this paper, a spoofing-aware speaker verification (SASV) system that integrates the automatic speaker verification (ASV) system and countermeasure (CM) system is developed. Firstly, a modified re-parameterized VGG (ARepVGG) module is utilized to extract high-level representation from the multi-scale feature that learns from the raw waveform though sinc-filters, and then a spectra-temporal graph attention network is used to learn the final decision information whether the audio is spoofed or not. Secondly, a new network that is inspired from the MaxFeature-Map (MFM) layers is constructed to fine-tune the CM system while keeping the ASV system fixed. Our proposed SASV system significantly improves the SASV equal error rate (SASV-EER) from 6.73% to 1.36% on the evaluation dataset and 4.85% to 0.98% on the development dataset in the 2022 Spoofing-Aware Speaker Verification Challenge(2022 SASV).
Cite as: Lin, J., Chen, T., Huang, J., Fang, R., Yin, J., Yin, Y., Shi, W., Huang, W., Mao, Y. (2022) The CLIPS System for 2022 Spoofing-Aware Speaker Verification Challenge. Proc. Interspeech 2022, 4367-4370, doi: 10.21437/Interspeech.2022-320
@inproceedings{lin22_interspeech,
author={Jucai Lin and Tingwei Chen and Jingbiao Huang and Ruidong Fang and Jun Yin and Yuanping Yin and Wei Shi and Weizhen Huang and Yapeng Mao},
title={{The CLIPS System for 2022 Spoofing-Aware Speaker Verification Challenge}},
year=2022,
booktitle={Proc. Interspeech 2022},
pages={4367--4370},
doi={10.21437/Interspeech.2022-320}
}