ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection

ASVspoof 2021: accelerating progress in spoofed and deepfake speech detection
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DOI:
10.21437/asvspoof.2021-8
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发表时间:
2021-09
期刊:
ArXiv
影响因子:
--
通讯作者:
J. Yamagishi;Xin Wang;M. Todisco;M. Sahidullah;J. Patino;A. Nautsch;Xuechen Liu;Kong Aik LEE;T. Kinnunen;N. Evans;H. Delgado
J. Yamagishi;Xin Wang;M. Todisco;M. Sahidullah;J. Patino;A. Nautsch;Xuechen Liu;Kong Aik LEE;T. Kinnunen;N. Evans;H. Delgado
中科院分区:
其他
文献类型:
--
作者:
J. Yamagishi;Xin Wang;M. Todisco;M. Sahidullah;J. Patino;A. Nautsch;Xuechen Liu;Kong Aik LEE;T. Kinnunen;N. Evans;H. Delgado

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ASVspoof 2021是两年一度的系列挑战中的第四版,旨在促进欺骗的研究和保护自动说话人验证系统免受操纵的对策的设计。除了继续关注逻辑和物理访问任务,与以前的版本相比,ASVspoof 2021还引入了一项涉及深度假语音检测的新任务。本文描述了这三项任务、每项任务的新数据库、评估指标、四个挑战基线、评估平台和挑战结果摘要。尽管引入了通道和压缩可变性,这增加了难度,但逻辑访问和深度伪任务的结果与以前的ASVspoof版本的结果相近。物理访问任务的结果表明,在真实的、可变的物理空间中检测攻击是困难的。ASVspoof 2021是第一个没有向参与者提供任何匹配的培训或发展数据的版本,这反映了永远无法自信地预测假冒和深度虚假演讲的性质的真实情况,结果非常令人鼓舞,表明近年来该领域取得了实质性进展。
ASVspoof 2021 is the forth edition in the series of bi-annual challenges which aim to promote the study of spoofing and the design of countermeasures to protect automatic speaker verification systems from manipulation. In addition to a continued focus upon logical and physical access tasks in which there are a number of advances compared to previous editions, ASVspoof 2021 introduces a new task involving deepfake speech detection. This paper describes all three tasks, the new databases for each of them, the evaluation metrics, four challenge baselines, the evaluation platform and a summary of challenge results. Despite the introduction of channel and compression variability which compound the difficulty, results for the logical access and deepfake tasks are close to those from previous ASVspoof editions. Results for the physical access task show the difficulty in detecting attacks in real, variable physical spaces. With ASVspoof 2021 being the first edition for which participants were not provided with any matched training or development data and with this reflecting real conditions in which the nature of spoofed and deepfake speech can never be predicated with confidence, the results are extremely encouraging and demonstrate the substantial progress made in the field in recent years.