  We address speaker-aware anti-spoofing, where prior knowledge of the target
speaker is incorporated into a voice spoofing countermeasure (CM). In contrast
to the frequently used speaker-independent solutions, we train the CM in a
speaker-conditioned way. As a proof of concept, we consider speaker-aware
extension to the state-of-the-art AASIST (audio anti-spoofing using integrated
spectro-temporal graph attention networks) model. To this end, we consider two
alternative strategies to incorporate target speaker information at the frame
and utterance levels, respectively. The experimental results on a custom
protocol based on ASVspoof 2019 dataset indicates the efficiency of the speaker
information via enrollment: we obtain maximum relative improvements of 25.1%
and 11.6% in equal error rate (EER) and minimum tandem detection cost function
(t-DCF) over a speaker-independent baseline, respectively.
