Domain-Aware Speaker Diarization On African-Accented English
This addresses speaker diarization challenges for African-accented English in clinical settings, though it is incremental with limited novelty.
This study examined domain effects in speaker diarization for African-accented English, finding a consistent domain penalty for clinical speech across models, with lightweight domain adaptation reducing error but not eliminating the gap.
This study examines domain effects in speaker diarization for African-accented English. We evaluate multiple production and open systems on general and clinical dialogues under a strict DER protocol that scores overlap. A consistent domain penalty appears for clinical speech and remains significant across models. Error analysis attributes much of this penalty to false alarms and missed detections, aligning with short turns and frequent overlap. We test lightweight domain adaptation by fine-tuning a segmentation module on accent-matched data; it reduces error but does not eliminate the gap. Our contributions include a controlled benchmark across domains, a concise approach to error decomposition and conversation-level profiling, and an adaptation recipe that is easy to reproduce. Results point to overlap-aware segmentation and balanced clinical resources as practical next steps.