DM-ASR reformulates multi-speaker ASR as multi-turn dialogue generation conditioned on diarization results, achieving competitive benchmark performance with relatively small models and limited data.
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LLMs detect social signals in clinical transcripts across model families, with an agreement-weighted ensemble using group-level agreement patterns improving accuracy and stability over individual models.
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DM-ASR: Diarization-aware Multi-speaker ASR with Large Language Models
DM-ASR reformulates multi-speaker ASR as multi-turn dialogue generation conditioned on diarization results, achieving competitive benchmark performance with relatively small models and limited data.
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SocialLM: Social Signal Processing of Patient-Provider Communication using LLMs and Contextual Aggregation
LLMs detect social signals in clinical transcripts across model families, with an agreement-weighted ensemble using group-level agreement patterns improving accuracy and stability over individual models.