Spoofing detection is today a mainstream research topic. Standard metrics can
be applied to evaluate the performance of isolated spoofing detection solutions
and others have been proposed to support their evaluation when they are
combined with speaker detection. These either have well-known deficiencies or
restrict the architectural approach to combine speaker and spoof detectors. In
this paper, we propose an architecture-agnostic detection cost function
(a-DCF). A generalisation of the original DCF used widely for the assessment of
automatic speaker verification (ASV), the a-DCF is designed for the evaluation
of spoofing-robust ASV. Like the DCF, the a-DCF reflects the cost of decisions
in a Bayes risk sense, with explicitly defined class priors and detection cost
model. We demonstrate the merit of the a-DCF through the benchmarking
evaluation of architecturally-heterogeneous spoofing-robust ASV solutions.

提出了一种适用于欺骗性溯源的 ASV 评估的通用检测成本函数（a-DCF），通过基于架构异构的欺骗性溯源固定 ASV 解决方案的基准评估，证明了 a-DCF 的优点。

a-DCF：一种与架构无关的度量标准，适用于抗欺诈的说话人验证

a-DCF: an architecture agnostic metric with application to  spoofing-robust speaker verification

Trained machine learning models are increasingly used to perform high-impact
tasks in areas such as law enforcement, medicine, education, and employment. In
order to clarify the intended use cases of machine learning models and minimize
their usage in contexts for which they are not well suited, we recommend that
released models be accompanied by documentation detailing their performance
characteristics. In this paper, we propose a framework that we call model
cards, to encourage such transparent model reporting. Model cards are short
documents accompanying trained machine learning models that provide benchmarked
evaluation in a variety of conditions, such as across different cultural,
demographic, or phenotypic groups (e.g., race, geographic location, sex,
Fitzpatrick skin type) and intersectional groups (e.g., age and race, or sex
and Fitzpatrick skin type) that are relevant to the intended application
domains. Model cards also disclose the context in which models are intended to
be used, details of the performance evaluation procedures, and other relevant
information. While we focus primarily on human-centered machine learning models
in the application fields of computer vision and natural language processing,
this framework can be used to document any trained machine learning model. To
solidify the concept, we provide cards for two supervised models: One trained
to detect smiling faces in images, and one trained to detect toxic comments in
text. We propose model cards as a step towards the responsible democratization
of machine learning and related AI technology, increasing transparency into how
well AI technology works. We hope this work encourages those releasing trained
machine learning models to accompany model releases with similar detailed
evaluation numbers and other relevant documentation.

本文提出了一个名为 “模型卡” 的框架，以鼓励对训练的机器学习模型进行透明的报告，并为不同应用领域提供基准评估。模型卡旨在推进机器学习和人工智能技术的负责任民主化，提高了解 AI 技术工作情况的透明度。