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

Abstract

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.

Accepted, Computer Speech and Language. This manuscript version is made available under the CC-BY-NC-ND 4.0. For the published version on Elsevier website, please visit https://doi.org/10.1016/j.csl.2020.101114

References in corpus (5)

• WaveNet: A Generative Model for Raw Audio
• Tutorial on Variational Autoencoders
• Generative Moment Matching Networks
• Voice Conversion Based on Cross-Domain Features Using Variational Auto Encoders
• WaveCycleGAN2: Time-domain Neural Post-filter for Speech Waveform Generation

Cited by in corpus (5)

• When the Differences in Frequency Domain are Compensated: Understanding and Defeating Modulated Replay Attacks on Automatic Speech Recognition
• The VoicePrivacy 2020 Challenge Evaluation Plan
• An explainability study of the constant Q cepstral coefficient spoofing countermeasure for automatic speaker verification
• Detection and Evaluation of human and machine generated speech in spoofing attacks on automatic speaker verification systems
• A Tandem Framework Balancing Privacy and Security for Voice User Interfaces