The CLIPS System for 2022 Spoofing-Aware Speaker Verification Challenge

Abstract

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 Max-Feature-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).