  Biometric systems are nowadays employed across a broad range of applications.
They provide high security and efficiency and, in many cases, are user
friendly. Despite these and other advantages, biometric systems in general and
Automatic speaker verification (ASV) systems in particular can be vulnerable to
attack presentations. The most recent ASVSpoof 2019 competition showed that
most forms of attacks can be detected reliably with ensemble classifier-based
presentation attack detection (PAD) approaches. These, though, depend
fundamentally upon the complementarity of systems in the ensemble. With the
motivation to increase the generalisability of PAD solutions, this paper
reports our exploration of texture descriptors applied to the analysis of
speech spectrogram images. In particular, we propose a common fisher vector
feature space based on a generative model. Experimental results show the
soundness of our approach: at most, 16 in 100 bona fide presentations are
rejected whereas only one in 100 attack presentations are accepted.
