SASV 2022: The First Spoofing-Aware Speaker Verification Challenge

TL;DR

The SASV 2022 challenge promotes integrated research in speaker verification and anti-spoofing, leading to highly effective solutions that significantly reduce error rates in spoofing scenarios.

Contribution

It introduces a unified challenge for jointly optimizing speaker verification and anti-spoofing, with open-source models and a new evaluation protocol.

Findings

Top system reduced EER from 23.83% to 0.13%.

23 teams participated, demonstrating state-of-the-art effectiveness.

Open-source models facilitated development of integrated solutions.

Abstract

The first spoofing-aware speaker verification (SASV) challenge aims to integrate research efforts in speaker verification and anti-spoofing. We extend the speaker verification scenario by introducing spoofed trials to the usual set of target and impostor trials. In contrast to the established ASVspoof challenge where the focus is upon separate, independently optimised spoofing detection and speaker verification sub-systems, SASV targets the development of integrated and jointly optimised solutions. Pre-trained spoofing detection and speaker verification models are provided as open source and are used in two baseline SASV solutions. Both models and baselines are freely available to participants and can be used to develop back-end fusion approaches or end-to-end solutions. Using the provided common evaluation protocol, 23 teams submitted SASV solutions. When assessed with target, bona fide non-target and spoofed non-target trials, the top-performing system reduces the equal error rate of a conventional speaker verification system from 23.83% to 0.13%. SASV challenge results are a testament to the reliability of today's state-of-the-art approaches to spoofing detection and speaker verification.