This paper defines Spoof Diarization as a novel task in the Partial Spoof
(PS) scenario. It aims to determine what spoofed when, which includes not only
locating spoof regions but also clustering them according to different spoofing
methods. As a pioneering study in spoof diarization, we focus on defining the
task, establishing evaluation metrics, and proposing a benchmark model, namely
the Countermeasure-Condition Clustering (3C) model. Utilizing this model, we
first explore how to effectively train countermeasures to support spoof
diarization using three labeling schemes. We then utilize spoof localization
predictions to enhance the diarization performance. This first study reveals
the high complexity of the task, even in restricted scenarios where only a
single speaker per audio file and an oracle number of spoofing methods are
considered. Our code is available at
this https URL

这篇研究定义了 Spoof Diarization 作为 Partial Spoof (PS) 情景下的一项新任务，旨在确定什么时候进行了欺骗行为，包括定位欺骗区域，并根据不同的欺骗方法进行聚类。作为 Spoof Diarization 领域的一项开创性研究，我们专注于定义任务、建立评估指标，并提出了一种基准模型，即 Countermeasure-Condition Clustering (3C) 模型。利用这个模型，我们首先探索了如何有效地训练反欺骗措施以支持 Spoof Diarization，使用了三种标签方案。然后，我们利用欺骗定位预测来提高对话划分的性能。这项研究首次揭示了这个任务的高复杂性，即使在仅考虑每个音频文件只有一个说话者和一个模拟方法的受限场景中也是如此。我们的代码可以在此 https URL 处获取。