  Voice anti-spoofing aims at classifying a given utterance either as a
bonafide human sample, or a spoofing attack (e.g. synthetic or replayed
sample). Many anti-spoofing methods have been proposed but most of them fail to
generalize across domains (corpora) -- and we do not know \emph{why}. We
outline a novel interpretative framework for gauging the impact of data quality
upon anti-spoofing performance. Our within- and between-domain experiments pool
data from seven public corpora and three anti-spoofing methods based on
Gaussian mixture and convolutive neural network models. We assess the impacts
of long-term spectral information, speaker population (through x-vector speaker
embeddings), signal-to-noise ratio, and selected voice quality features.
