DG^VoiC

DG^VoiC: Speaker clustering framework for fraud detection in call-centre audio

Researchers present DG^VoiC, a voice clustering framework designed to identify repeated speakers across anonymised call-centre recordings for insurance fraud investigation. The system combines anonymisation-aligned preprocessing, sliding-window speaker embeddings, and cosine similarity clustering, evaluated on 121 real telephony recordings. On a curated 56-sample reference set, the best configuration achieves 96% AMI, 95% ARI, and 100% homogeneity, suggesting speaker identity is a viable underutilised signal for fraud detection workflows.