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The first spoofing-aware speaker verification (SASV) challenge aims tointegrate research efforts in speaker verification and anti-spoofing. We extendthe speaker verification scenario by introducing spoofed trials to the usualset of target and impostor trials. In contrast to the established ASVspoofchallenge where the focus is upon separate, independently optimised spoofingdetection and speaker verification sub-systems, SASV targets the development ofintegrated and jointly optimised solutions. Pre-trained spoofing detection andspeaker verification models are provided as open source and are used in twobaseline SASV solutions. Both models and baselines are freely available toparticipants and can be used to develop back-end fusion approaches orend-to-end solutions. Using the provided common evaluation protocol, 23 teamssubmitted SASV solutions. When assessed with target, bona fide non-target andspoofed non-target trials, the top-performing system reduces the equal errorrate of a conventional speaker verification system from 23.83% to 0.13%. SASVchallenge results are a testament to the reliability of today'sstate-of-the-art approaches to spoofing detection and speaker verification.

SASV 2022: The First Spoofing-Aware Speaker Verification Challenge