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

Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing