  Current state-of-the-art automatic speaker verification (ASV) systems are
vulnerable to presentation attacks, and several countermeasures (CMs), which
distinguish bona fide trials from spoofing ones, have been explored to protect
ASV. However, ASV systems and CMs are generally developed and optimized
independently without considering their inter-relationship. In this paper, we
propose a new spoofing-aware ASV back-end module that efficiently computes a
combined ASV score based on speaker similarity and CM score. In addition to the
learnable fusion function of the two scores, the proposed back-end module has
two types of attention components, scaled-dot and feed-forward self-attention,
so that intra-relationship information of multiple enrollment utterances can
also be learned at the same time. Moreover, a new effective trials-sampling
strategy is designed for simulating new spoofing-aware verification scenarios
introduced in the Spoof-Aware Speaker Verification (SASV) challenge 2022.
