CBAL: Context-Based Agentic Learning for Speaker Diarization Segmentation Refinement (2026.acl-srw)
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| Challenge: | Speaker diarization systems produce segmentation errors that degrade transcript readability and downstream applications. |
| Approach: | They propose a framework that refines segmentation boundaries in diarized scripts . they use a lightweight LLM agent to reason about merge decisions . |
| Outcome: | The proposed framework achieves 93.4% accuracy across 359 applied merges and reduces segment count by 6.1%. |
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Amir Saeidi, Venkatesh Mishra, Souradeep Mukhopadhyay, Gaowen Liu, Ali Payani, Jayanth Srinivasa, Chitta Baral
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