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CBAL: Context-Based Agentic Learning for Speaker Diarization Segmentation Refinement
来源日期（仅到月份） 2026-07
来源记录日期 2026-07
雷达观测 2026-11-01 21:44
摘要
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.
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记录 #1 · 2026-07 · 最新观测版本
CBAL: Context-Based Agentic Learning for Speaker Diarization Segmentation Refinement
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.
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