  The threat of spoofing can pose a risk to the reliability of automatic
speaker verification. Results from the bi-annual ASVspoof evaluations show that
effective countermeasures demand front-ends designed specifically for the
detection of spoofing artefacts. Given the diversity in spoofing attacks,
ensemble methods are particularly effective. The work in this paper shows that
a bank of very simple classifiers, each with a front-end tuned to the detection
of different spoofing attacks and combined at the score level through
non-linear fusion, can deliver superior performance than more sophisticated
ensemble solutions that rely upon complex neural network architectures. Our
comparatively simple approach outperforms all but 2 of the 48 systems submitted
to the logical access condition of the most recent ASVspoof 2019 challenge.
