论文标题
在自动扬声器验证中推断错误警报率
Extrapolating false alarm rates in automatic speaker verification
论文作者
论文摘要
自动扬声器验证（ASV）供应商和语料库提供商都将受益于工具，可在不收集新扬声器的情况下可靠地推断出大型扬声器人群的性能指标。我们解决了最坏情况模型下的虚假警报率外推，从而使对手确定了来自大量人群的给定目标扬声器的最接近的冒名顶替者。我们的模型是生成的，允许对新扬声器进行采样。这些模型是在ASV检测得分空间中配制的，以促进分析任意ASV系统。
Automatic speaker verification (ASV) vendors and corpus providers would both benefit from tools to reliably extrapolate performance metrics for large speaker populations without collecting new speakers. We address false alarm rate extrapolation under a worst-case model whereby an adversary identifies the closest impostor for a given target speaker from a large population. Our models are generative and allow sampling new speakers. The models are formulated in the ASV detection score space to facilitate analysis of arbitrary ASV systems.
