Over the past few years significant progress has been made in the field of presentation attack detection (PAD) for automatic speaker recognition (ASV). This includes the development of new speech corpora, standard evaluation protocols and advancements in front-end feature extraction and back-end classifiers. The use of standard databases and evaluation protocols has enabled for the first time the meaningful benchmarking of different PAD solutions. This chapter summarises the progress, with a focus on studies completed in the last three years. The article presents a summary of findings and lessons learned from two ASVspoof challenges, the first community-led benchmarking efforts. These show that ASV PAD remains an unsolved problem and that further attention is required to develop generalised PAD solutions which have potential to detect diverse and previously unseen spoofing attacks.
翻译:过去几年来,在自动识别扬声器的演示攻击探测(PAD)领域取得了显著进展,包括开发了新的语音组合、标准评价规程和前端特征提取和后端分类器的进步;使用标准数据库和评价规程首次使不同的PAD解决方案有了有意义的基准;本章总结了进展情况,重点是过去三年完成的研究;文章总结了从两个ASVspoof挑战中得出的调查结果和经验教训,这是社区牵头的第一个基准工作;这些都表明,ASVPAD仍然是一个尚未解决的问题,需要进一步注意制定通用的PAD解决方案,这些解决方案有可能发现各种先前看不见的攻击。