AI RESEARCH
Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization
arXiv CS.CL
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ArXi:2603.00086v2 Announce Type: replace Automatic speech recognition for French medical conversations remains challenging, with word error rates often exceeding 30% in spontaneous clinical speech. This study proposes a multi-pass LLM post-processing architecture alternating between Speaker Recognition and Word Recognition passes to improve transcription accuracy and speaker attribution.