  Many endeavors have sought to develop countermeasure techniques as
enhancements on Automatic Speaker Verification (ASV) systems, in order to make
them more robust against spoof attacks. As evidenced by the latest ASVspoof
2019 countermeasure challenge, models currently deployed for the task of ASV
are, at their best, devoid of suitable degrees of generalization to unseen
attacks. Upon further investigation of the proposed methods, it appears that a
broader three-tiered view of the proposed systems. comprised of the classifier,
feature extraction phase, and model loss function, may to some extent lessen
the problem. Accordingly, the present study proposes the Efficient Attention
Branch Network (EABN) modular architecture with a combined loss function to
address the generalization problem...
