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.
Visualizing classifier adjacency relations: A case study in speaker verification and voice anti-spoofing
INTERSPEECH 2021, Conference of the International Speech Communication Association, 30 August-3 September 2021, Brno, Czechia (Virtual Conference)
© ISCA. Personal use of this material is permitted. The definitive version of this paper was published in INTERSPEECH 2021, Conference of the International Speech Communication Association, 30 August-3 September 2021, Brno, Czechia (Virtual Conference) and is available at : http://dx.doi.org/10.21437/Interspeech.2021-1522
PERMALINK : https://www.eurecom.fr/publication/6584