Odyssey 2018 - ASVspoof 2017 Version 2.0: meta-data analysis and baseline enhancements

Spoofing

Héctor Delgado, Massimiliano Todisco, Md Sahidullah, Nicholas Evans, Tomi Kinnunen, Kong Aik Lee and Junichi Yamagishi

The now-acknowledged vulnerabilities of automatic speaker verification (ASV) technology to spoofing attacks have spawned interests to develop so-called spoofing countermeasures. By providing common databases, protocols and metrics for their assessment, the ASVspoof initiative was born to spearhead research in this area. The first competitive ASVspoof challenge held in 2015 focused on the assessment of countermeasures to protect ASV technology from voice conversion and speech synthesis spoofing attacks. The second challenge switched focus to the consideration of replay spoofing attacks and countermeasures. This paper describes Version 2.0 of the ASVspoof 2017 database which was released to correct data anomalies detected post-evaluation. The paper contains as-yet unpublished meta-data which describes recording and replay devices and acoustic environments. These support the analysis of replay detection performance and limits. Also described are new results for the official ASVspoof baseline system which is based upon a constant Q cesptral coefficient frontend and a Gaussian mixture model backend. Reported are enhancements to the baseline system in the form of log-energy coefficients and cepstral mean and variance normalisation in addition to an alternative iVector backend. The best results correspond to a 48% relative reduction in equal error rate when compared to the original baseline system.

Cite as: Delgado, H., Todisco, M., Sahidullah, M., Evans, N., Kinnunen, T., Lee, K.A., Yamagishi, J. (2018) ASVspoof 2017 Version 2.0: meta-data analysis and baseline enhancements . Proc. Odyssey 2018 The Speaker and Language Recognition Workshop, 296-303, DOI: 10.21437/Speaker Odyssey.2018-42.