通过语音聚类识别保险欺诈中的重复嫌疑人，提升调查效率。
DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions
- 用语音嵌入与相似度聚类，在匿名通话中识别同一人多次出现。
- 在121段录音上达到96%的AMI和99%的V-measure，效果优异。
- 适合反欺诈团队用于跨客户语音关联分析。
保险欺诈成本高昂且难以侦测，尤其在理赔初审（FNOL）阶段的电话中心场景中。现有检测方法多依赖结构化数据、文本或图像，而通话中重复的说话人身份这一信号尚未被充分利用。本文提出DG^VoiC，一种针对匿名真实电话中心音频的语音聚类框架，用于客户身份验证与跨账号说话人关联。该方法结合敏感信息对齐的匿名化、聚焦语音的预处理、滑动窗口嵌入提取及余弦相似度聚类，在121段录音上进行评估，其中56个样本构成22个经人工标注的说话人簇作为参考。最佳配置下，AMI达96%，ARI为95%，完整性98%，同质性100%，V-measure为99%。结果表明，语音聚类可为反欺诈调查提供有力补充信号，帮助分析师验证说话人一致性并发现跨账户的重复声音。
原文摘要 · Abstract (English)
Insurance fraud remains costly and operationally difficult, particularly in call-centre workflows where many customer interactions begin at FNOL. While recent fraud detection methods mainly rely on structured data, text, or images, repeated speaker identity across calls remains underused as an investigative signal. This paper presents DG^VoiC, a voice clustering framework for customer verification and cross-profile speaker linking on anonymised real call-centre audio. The approach combines sensitive information-aligned anonymisation, speech-focused preprocessing, sliding-window speaker embedding extraction, and cosine similarity based clustering to identify repeated speakers under real telephony conditions. The method was evaluated on 121 recordings, with a curated reference subset of 56 samples in 22 human-agreed speaker clusters. used for validation. The best configuration achieved 96% AMI, 95% ARI, 98% completeness, 100% homogeneity, and 99% V-measure. These results show that speaker clustering can provide a strong additional signal for fraud investigation by helping analysts verify speaker consistency and surface repeated voices across customers.
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