  A great deal of recent research effort on speech spoofing countermeasures has
been invested into back-end neural networks and training criteria. We
contribute to this effort with a comparative perspective in this study. Our
comparison of countermeasure models on the ASVspoof 2019 logical access
scenario takes into account common strategies to deal with input trials of
varied length, recently proposed margin-based training criteria, and widely
used front ends. We also measured intra-model differences through multiple
training-evaluation rounds with random initialization. Our statistical analysis
demonstrates that the performance of the same model may be statistically
significantly different when just changing the random initial seed. We thus
recommend similar statistical analysis or reporting results of multiple runs
for further research on the database. Despite the intra-model differences, we
observed a few promising techniques, including average pooling, to efficiently
process varied-length inputs and a new hyper-parameter-free loss function. The
two techniques led to the best single model in our experiment, which achieved
an equal error rate of 1.92% and was significantly different in statistical
sense from most of the other experimental models.
