  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.
