  This paper describes the Speech Technology Center (STC) antispoofing systems
submitted to the ASVspoof 2019 challenge. The ASVspoof2019 is the extended
version of the previous challenges and includes 2 evaluation conditions:
logical access use-case scenario with speech synthesis and voice conversion
attack types and physical access use-case scenario with replay attacks. During
the challenge we developed anti-spoofing solutions for both scenarios. The
proposed systems are implemented using deep learning approach and are based on
different types of acoustic features. We enhanced Light CNN architecture
previously considered by the authors for replay attacks detection and which
performed high spoofing detection quality during the ASVspoof2017 challenge. In
particular here we investigate the efficiency of angular margin based softmax
activation for training robust deep Light CNN classifier to solve the
mentioned-above tasks. Submitted systems achieved EER of 1.86% in logical
access scenario and 0.54% in physical access scenario on the evaluation part of
the Challenge corpora. High performance obtained for the unknown types of
spoofing attacks demonstrates the stability of the offered approach in both
evaluation conditions.
