Paper Info
Title
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.
Year
DOI
Venue
2021
10.21437/Interspeech.2021-1522
Interspeech
DocType
Citations 
PageRank 
Conference
0
0.34
References 
Authors
0
9
Name
Order
Citations
PageRank
Tomi Kinnunen1132386.67
Andreas Nautsch24410.92
Md. Sahidullah332624.99
nicholas evans459454.41
Xin Wang5132.36
Massimiliano Todisco613817.80
hector delgado77810.47
Junichi Yamagishi8172.10
Kong-Aik Lee970960.64