Benchmarking initiatives support the meaningful comparison of competing solutions to prominent problems in speech and language processing. Successive benchmarking evaluations typically reflect a progressive evolution from ideal lab conditions towards to those encountered in the wild. ASVspoof, the spoofing and deepfake detection initiative and challenge series, has followed the same trend. This article provides a summary of the ASVspoof 2021 challenge and the results of 54 participating teams that submitted to the evaluation phase. For the logical access (LA) task, results indicate that countermeasures are robust to newly introduced encoding and transmission effects. Results for the physical access (PA) task indicate the potential to detect replay attacks in real, as opposed to simulated physical spaces, but a lack of robustness to variations between simulated and real acoustic environments. The Deepfake (DF) task, new to the 2021 edition, targets solutions to the detection of manipulated, compressed speech data posted online. While detection solutions offer some resilience to compression effects, they lack generalization across different source datasets. In addition to a summary of the top-performing systems for each task, new analyses of influential data factors and results for hidden data subsets, the article includes a review of post-challenge results, an outline of the principal challenge limitations and a road-map for the future of ASVspoof.

翻译：基准评测项目支持对语音和语言处理中重要问题的各类解决方案进行有意义的比较。此类评测通常反映从理想实验室条件向现实场景的渐进演进趋势。反欺骗与深度伪造检测倡议及挑战系列ASVspoof亦遵循相同发展路径。本文综述了ASVspoof 2021挑战赛及54支参赛团队提交至评估阶段的结果。在逻辑访问（LA）任务中，结果表明反制措施对新型编码与传输效应具有鲁棒性。物理访问（PA）任务结果表明，在真实物理空间（而非模拟环境）中检测回放攻击具有可行性，但系统对模拟与真实声学环境差异的鲁棒性仍显不足。2021年新增的深度伪造（DF）任务聚焦于检测通过压缩处理后发布的在线伪造语音数据。尽管检测方案对压缩效应具备一定弹性，但其在不同源数据集间的泛化能力不足。除总结各任务最优系统外，本文还分析了关键数据因素的影响及隐藏数据子集的结果，并回顾了挑战赛后成果、概述了本次挑战的主要局限，同时规划了ASVspoof的未来发展路线图。