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This paper describes the Speech Technology Center (STC) antispoofing systemssubmitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extendedversion of the previous challenges and includes 2 evaluation conditions:logical access use-case scenario with speech synthesis and voice conversionattack types and physical access use-case scenario with replay attacks. Duringthe challenge we developed anti-spoofing solutions for both scenarios. Theproposed systems are implemented using deep learning approach and are based ondifferent types of acoustic features. We enhanced Light CNN architecturepreviously considered by the authors for replay attacks detection and whichperformed high spoofing detection quality during the ASVspoof2017 challenge. Inparticular here we investigate the efficiency of angular margin based softmaxactivation for training robust deep Light CNN classifier to solve thementioned-above tasks. Submitted systems achieved EER of 1.86% in logicalaccess scenario and 0.54% in physical access scenario on the evaluation part ofthe Challenge corpora. High performance obtained for the unknown types ofspoofing attacks demonstrates the stability of the offered approach in bothevaluation conditions.

STC Antispoofing Systems for the ASVspoof2019 Challenge