  We present our analysis of a significant data artifact in the official
2019/2021 ASVspoof Challenge Dataset. We identify an uneven distribution of
silence duration in the training and test splits, which tends to correlate with
the target prediction label. Bonafide instances tend to have significantly
longer leading and trailing silences than spoofed instances. In this paper, we
explore this phenomenon and its impact in depth. We compare several types of
models trained on a) only the duration of the leading silence and b) only on
the duration of leading and trailing silence. Results show that models trained
on only the duration of the leading silence perform particularly well, and
achieve up to 85% percent accuracy and an equal error rate (EER) of 15.1%. At
the same time, we observe that trimming silence during pre-processing and then
training established antispoofing models using signal-based features leads to
comparatively worse performance. In that case, EER increases from 3.6% (with
silence) to 15.5% (trimmed silence). Our findings suggest that previous work
may, in part, have inadvertently learned thespoof/bonafide distinction by
relying on the duration of silence as it appears in the official challenge
dataset. We discuss the potential consequences that this has for interpreting
system scores in the challenge and discuss how the ASV community may further
consider this issue.
