  We present JHU's system submission to the ASVspoof 2019 Challenge:
Anti-Spoofing with Squeeze-Excitation and Residual neTworks (ASSERT).
Anti-spoofing has gathered more and more attention since the inauguration of
the ASVspoof Challenges, and ASVspoof 2019 dedicates to address attacks from
all three major types: text-to-speech, voice conversion, and replay. Built upon
previous research work on Deep Neural Network (DNN), ASSERT is a pipeline for
DNN-based approach to anti-spoofing. ASSERT has four components: feature
engineering, DNN models, network optimization and system combination, where the
DNN models are variants of squeeze-excitation and residual networks. We
conducted an ablation study of the effectiveness of each component on the
ASVspoof 2019 corpus, and experimental results showed that ASSERT obtained more
than 93% and 17% relative improvements over the baseline systems in the two
sub-challenges in ASVspooof 2019, ranking ASSERT one of the top performing
systems. Code and pretrained models will be made publicly available.
