DG^VoiC: Speaker Clustering for Fraud Investigation under Real Call-Centre Conditions
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 te...
| Authors | Muhammad Shakeel Akram, Amal Htait, Abdul Hamid Sadka et al. |
|---|---|
| Primary category | cs.SD |
| Published | 26 June 2026 |
| Last updated at source | 26 June 2026 |
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