  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 task
takes into account recently proposed margin-based training criteria, widely
used front ends, and common strategies to deal with varied-length input trials.
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 significantly different when just
changing the random initial seed. Thus, we recommend similar analysis or
multiple training-evaluation rounds for further research on the database.
Despite the intra-model differences, we observed a few promising techniques
such as the average pooling to 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.
