[2603.00086] Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization

Computer Science > Computation and Language arXiv:2603.00086 (cs) [Submitted on 16 Feb 2026] Title:Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization Authors:Ambre Marie (LaTIM), Thomas Bertin (DySoLab), Guillaume Dardenne (LaTIM), Gwenolé Quellec (LaTIM) View a PDF of the paper titled Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization, by Ambre Marie (LaTIM) and 3 other authors View PDF Abstract: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. Ablation studies on two French clinical datasets (suicide prevention telephone counseling and preoperative awake neurosurgery consultations) investigate four design choices: model selection, prompting strategy, pass ordering, and iteration depth. Using Qwen3-Next-80B, Wilcoxon signed-rank tests confirm significant WDER reductions on suicide prevention conversations (p < 0.05, n=18), while maintaining stability on awake neurosurgery consultations (n=10), with zero output failures and acceptable computational cost (RTF 0.32), suggesting feasibility for offline clinical deployment. Subjects: Computation and Language (cs.CL); Artificial Intellig...