The ASVspoof 2019 database

by   Xin Wang, et al.

Automatic speaker verification (ASV) is one of the most natural and convenient means of biometric person recognition. Unfortunately, just like all other biometric systems, ASV is vulnerable to spoofing, 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 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.


page 11

page 13

page 18

page 19

page 20


STC Antispoofing Systems for the ASVspoof2019 Challenge

This paper describes the Speech Technology Center (STC) antispoofing sys...

ASVspoof 2019: Future Horizons in Spoofed and Fake Audio Detection

ASVspoof, now in its third edition, is a series of community-led challen...

ASVspoof 2019: spoofing countermeasures for the detection of synthesized, converted and replayed speech

The ASVspoof initiative was conceived to spearhead research in anti-spoo...

Generalization of Spoofing Countermeasures: a Case Study with ASVspoof 2015 and BTAS 2016 Corpora

Voice-based biometric systems are highly prone to spoofing attacks. Rece...

The PartialSpoof Database and Countermeasures for the Detection of Short Generated Audio Segments Embedded in a Speech Utterance

Automatic speaker verification is susceptible to various manipulations a...

Spoof detection using x-vector and feature switching

Detecting spoofed utterances is a fundamental problem in voice-based bio...

Physiological-Physical Feature Fusion for Automatic Voice Spoofing Detection

Speaker verification systems have been used in many production scenarios...