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ASVspoof 2019: A large-scale public database of synthetized, converted and replayed speech

机译：asvspoof 2019：综合，转换和重放演讲的大规模公共数据库

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Automatic speaker verification (ASV) is one of the most natural and convenient means of bio-metric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spooring, also referred to as "presentation attacks." These vulnerabilities are generally unacceptable and call for spoofing countermeasures or "presentation attack detection" systems. In addition to impersonation, ASV systems are vulnerable to replay, speech synthesis, and voice conversion attacks. The ASVspoof challenge initiative was created to foster research on anti-spoofing and to provide common platforms for the assessment and comparison of spoofing countermeasures. The first edition, ASVspoof 2015, focused upon the study of countermeasures for detecting of text-to-speech synthesis (TTS) and voice conversion (VC) attacks. The second edition, ASVspoof 2017, focused instead upon replay spooring attacks and countermeasures. The ASVspoof 2019 edition is the first to consider all three spoofing attack types within a single challenge. While they originate from the same source database and same underlying protocol, they are explored in two specific use case scenarios. Spoofing attacks within a logical access (LA) scenario are generated with the latest speech synthesis and voice conversion technologies, including state-of-the-art neural acoustic and waveform model techniques. Replay spoofing attacks within a physical access (PA) scenario are generated through carefully controlled simulations that support much more revealing analysis than possible previously. Also new to the 2019 edition is the use of the tandem detection cost function metric, which reflects the impact of spoofing and countermeasures on the reliability of a fixed ASV system. This paper describes the database design, protocol, spoofing attack implementations, and baseline ASV and countermeasure results. It also describes a human assessment on spoofed data in logical access. It was demonstrated that the spoofing data in the ASVspoof 2019 database have varied degrees of perceived quality and similarity to the target speakers, including spoofed data that cannot be differentiated from bona fide utterances even by human subjects. It is expected that the ASVspoof 2019 database, with its varied coverage of different types of spoofing data, could further foster research on anti-spoofing.
机译：自动扬声器验证（ASV）是生物公制人士认可最自然和方便的手段之一。不幸的是，就像所有其他生物识别系统一样，ASV容易受到麻木，也称为“呈现攻击”。这些漏洞通常是不可接受的，并呼吁欺骗对策或“呈现攻击检测”系统。除了冒充外，ASV系统易于重播，语音合成和语音转换攻击。创建了ASVSpof挑战计划，以促进对抗欺骗的研究，并为评估和比较欺骗对策提供共同平台。第一版，ASVSPOOO 2015，专注于研究检测文本到语音合成（TTS）和语音转换（VC）攻击的对策。第二版，Asvspoof 2017，相反，重播了重放的麻托攻击和对策。 ASVSpoof 2019 Edition是第一个考虑在单一挑战中的所有三种欺骗攻击类型。虽然它们来自相同的源数据库和相同的底层协议，但它们在两个特定用例方案中探讨它们。利用最新语音合成和语音转换技术产生逻辑访问（LA）方案内的欺骗攻击，包括最先进的神经声学和波形模型技术。通过仔细控制模拟来生成物理访问（PA）方案中的重放欺骗攻击，这些模拟比以前更容易进行更大的露出分析。 2019年版的新型也是使用串联检测成本函数指标，这反映了欺骗和对策对固定ASV系统的可靠性的影响。本文介绍了数据库设计，协议，欺骗攻击实现和基线ASV和对策结果。它还描述了对逻辑访问中欺骗数据的人性评估。有人证明，ASVSpof 2019数据库中的欺骗数据具有多种的感知质量和与目标扬声器的相似性，包括甚至通过人类受试者甚至不能从真人般的话语中区别区别的欺骗数据。预计ASVSPOOF 2019数据库，其不同类型的欺骗数据的覆盖率，可以进一步促进对抗欺骗的研究。

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