Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing
Abstract
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.
Cite
@article{arxiv.2106.06362,
title = {Visualizing Classifier Adjacency Relations: A Case Study in Speaker Verification and Voice Anti-Spoofing},
author = {Tomi Kinnunen and Andreas Nautsch and Md Sahidullah and Nicholas Evans and Xin Wang and Massimiliano Todisco and Héctor Delgado and Junichi Yamagishi and Kong Aik Lee},
journal= {arXiv preprint arXiv:2106.06362},
year = {2021}
}
Comments
Accepted to Interspeech 2021. Example code available at https://github.com/asvspoof-challenge/classifier-adjacency