t-DCF: a Detection Cost Function for the Tandem Assessment of Spoofing Countermeasures and Automatic Speaker Verification

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Authors: Tomi Kinnunen, Kong Aik Lee, Hector Delgado, Nicholas Evans, Massimiliano Todisco, Md Sahidullah, Junichi Yamagishi, Douglas A. Reynolds

Published: 2018-04-25 15:16:48+00:00

AI Summary

This paper introduces a new tandem detection cost function (t-DCF) metric for evaluating anti-spoofing countermeasures in automatic speaker verification (ASV). The t-DCF improves upon the equal error rate (EER) by considering the costs of different errors and prior probabilities of target and spoof trials, leading to more realistic and application-specific performance assessment.

Abstract

The ASVspoof challenge series was born to spearhead research in anti-spoofing for automatic speaker verification (ASV). The two challenge editions in 2015 and 2017 involved the assessment of spoofing countermeasures (CMs) in isolation from ASV using an equal error rate (EER) metric. While a strategic approach to assessment at the time, it has certain shortcomings. First, the CM EER is not necessarily a reliable predictor of performance when ASV and CMs are combined. Second, the EER operating point is ill-suited to user authentication applications, e.g. telephone banking, characterised by a high target user prior but a low spoofing attack prior. We aim to migrate from CM- to ASV-centric assessment with the aid of a new tandem detection cost function (t-DCF) metric. It extends the conventional DCF used in ASV research to scenarios involving spoofing attacks. The t-DCF metric has 6 parameters: (i) false alarm and miss costs for both systems, and (ii) prior probabilities of target and spoof trials (with an implied third, nontarget prior). The study is intended to serve as a self-contained, tutorial-like presentation. We analyse with the t-DCF a selection of top-performing CM submissions to the 2015 and 2017 editions of ASVspoof, with a focus on the spoofing attack prior. Whereas there is little to choose between countermeasure systems for lower priors, system rankings derived with the EER and t-DCF show differences for higher priors. We observe some ranking changes. Findings support the adoption of the DCF-based metric into the roadmap for future ASVspoof challenges, and possibly for other biometric anti-spoofing evaluations.

Key findings

The t-DCF metric revealed differences in ranking of countermeasure systems compared to the EER metric, especially for higher spoofing attack priors. Results support the adoption of t-DCF for future ASVspoof challenges to provide a more realistic assessment of anti-spoofing performance in tandem with ASV systems.

Approach

The authors propose a tandem detection cost function (t-DCF) metric that extends the conventional DCF to scenarios with spoofing attacks. The t-DCF incorporates costs associated with false alarms and misses for both ASV and countermeasure systems, along with prior probabilities of target, non-target, and spoof trials. This allows for a more comprehensive evaluation of the combined system performance.

Datasets

ASVspoof 2015 and ASVspoof 2017 datasets.

Model(s)

GMM-UBM (Gaussian Mixture Model - Universal Background Model) for ASV. Various countermeasure models submitted to the ASVspoof challenges were evaluated.

Author countries

Finland, Japan, France, France, Japan, U.K., USA

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