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Automatic speaker verification (ASV) is one of the most natural andconvenient means of biometric person recognition. Unfortunately, just like allother biometric systems, ASV is vulnerable to spoofing, also referred to as"presentation attacks." These vulnerabilities are generally unacceptable andcall for spoofing countermeasures or "presentation attack detection" systems.In addition to impersonation, ASV systems are vulnerable to replay, speechsynthesis, and voice conversion attacks. The ASVspoof 2019 edition is the firstto consider all three spoofing attack types within a single challenge. Whilethey originate from the same source database and same underlying protocol, theyare explored in two specific use case scenarios. Spoofing attacks within alogical access (LA) scenario are generated with the latest speech synthesis andvoice conversion technologies, including state-of-the-art neural acoustic andwaveform model techniques. Replay spoofing attacks within a physical access(PA) scenario are generated through carefully controlled simulations thatsupport much more revealing analysis than possible previously. Also new to the2019 edition is the use of the tandem detection cost function metric, whichreflects the impact of spoofing and countermeasures on the reliability of afixed ASV system. This paper describes the database design, protocol, spoofingattack implementations, and baseline ASV and countermeasure results. It alsodescribes a human assessment on spoofed data in logical access. It wasdemonstrated that the spoofing data in the ASVspoof 2019 database have varieddegrees of perceived quality and similarity to the target speakers, includingspoofed data that cannot be differentiated from bona-fide utterances even byhuman subjects.

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