  Voice anti-spoofing aims at classifying a given speech input either as a
bonafide human sample, or a spoofing attack (e.g. synthetic or replayed
sample). Numerous voice 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.
