@inproceedings{dutta-vishwakarma-2026-cbal,
    title = "{CBAL}: Context-Based Agentic Learning for Speaker Diarization Segmentation Refinement",
    author = "Dutta, Odwitiyo  and
      Vishwakarma, Dinesh K",
    editor = "T.Y.S.S., Santosh  and
      Rodriguez, Juan Diego  and
      de Gibert, Ona",
    booktitle = "Proceedings of the 64th Annual Meeting of the {A}ssociation for {C}omputational {L}inguistics ({ACL} 2026)",
    month = jul,
    year = "2026",
    address = "San Diego, California, United States",
    publisher = "Association for Computational Linguistics",
    url = "https://preview.aclanthology.org/ingest-acl/2026.acl-srw.58/",
    pages = "632--647",
    ISBN = "979-8-89176-393-7",
    abstract = "Speaker diarization systems produce segmentation errors, such as false splits and boundary misplacements, that degrade transcript readability and downstream applications. We present CBAL (Context-Based Agentic Learning), a post-processing framework that refines segmentation boundaries in diarized scripts through targeted error correction. CBAL detects potential segmentation errors using acoustic and temporal heuristics and employs a lightweight LLM agent to reason about merge decisions, validating corrections through uncertainty-aware filtering with signal-based constraints. CBAL achieves 93.4{\%} accuracy across 359 applied merges and reduces segment count by 6.1{\%}. We demonstrate that our framework identifies and corrects high-confidence errors while maintaining 0{\%} degradation in terms of concatenated minimum-permutation Word Error Rate (cpWER). An ablation study confirms that each component contributes non-redundantly, demonstrating the viability of interpretable refinement frameworks that use the strengths of acoustic models and language understanding without requiring end-to-end retraining."
}