  Modern automatic speaker verification (ASV) relies heavily on machine
learning implemented through deep neural networks. It can be difficult to
interpret the output of these black boxes. In line with interpretative machine
learning, we model the dependency of ASV detection score upon acoustic mismatch
of the enrollment and test utterances. We aim to identify mismatch factors that
explain target speaker misses (false rejections). We use distance in the first-
and second-order statistics of selected acoustic features as the predictors in
a linear mixed effects model, while a standard Kaldi x-vector system forms our
ASV black-box. Our results on the VoxCeleb data reveal the most prominent
mismatch factor to be in F0 mean, followed by mismatches associated with
formant frequencies. Our findings indicate that x-vector systems lack
robustness to intra-speaker variations.
