  Whether it be for results summarization, or the analysis of classifier
fusion, some means to compare different classifiers can often provide
illuminating insight into their behaviour, (dis)similarity or complementarity.
We propose a simple method to derive 2D representation from detection scores
produced by an arbitrary set of binary classifiers in response to a common
dataset. Based upon rank correlations, our method facilitates a visual
comparison of classifiers with arbitrary scores and with close relation to
receiver operating characteristic (ROC) and detection error trade-off (DET)
analyses. While the approach is fully versatile and can be applied to any
detection task, we demonstrate the method using scores produced by automatic
speaker verification and voice anti-spoofing systems. The former are produced
by a Gaussian mixture model system trained with VoxCeleb data whereas the
latter stem from submissions to the ASVspoof 2019 challenge.
