Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization
arXiv:2603.00086v1 Announce Type: new 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.
arXiv:2603.00086v1 Announce Type: new 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.
Executive Summary
This study addresses a critical gap in French clinical speech transcription by introducing an iterative LLM-based post-processing framework that alternates between speaker recognition and word recognition passes to enhance both transcription accuracy and speaker attribution. Given the persistent challenge of high word error rates (WER) exceeding 30% in spontaneous French clinical dialogue, the proposed architecture offers a systematic, multi-pass correction strategy. Ablation studies on two distinct clinical datasets—suicide prevention telephone counseling and preoperative awake neurosurgery consultations—evaluate four key design parameters: model selection, prompting strategy, pass ordering, and iteration depth. The results, validated via Wilcoxon signed-rank tests, demonstrate statistically significant reductions in WER for suicide prevention conversations (p < 0.05, n=18), while exhibiting stability in the neurosurgery cohort (n=10). Notably, the system maintains acceptable computational efficiency (RTF 0.32) and zero output failures, indicating viability for offline clinical deployment. The work contributes a scalable, iterative correction mechanism tailored to linguistic complexities in medical French.
Key Points
- ▸ Iterative LLM post-processing improves transcription accuracy and speaker attribution in French clinical speech.
- ▸ Ablation studies on two datasets assess four design variables: model choice, prompting, pass order, and iteration depth.
- ▸ Statistically significant WER reductions confirmed via Wilcoxon tests in suicide prevention conversations, with stability in neurosurgery data.
Merits
Strength of Methodology
The use of a multi-pass iterative LLM framework introduces a novel, systematic approach to post-processing that directly targets transcription and speaker attribution issues in complex clinical speech.
Demerits
Limitation in Generalizability
While results are promising on the tested datasets, the study’s focus on specific French clinical contexts (suicide prevention and neurosurgery) may limit applicability to broader clinical domains or languages without additional validation.
Expert Commentary
The authors present a compelling, empirically validated solution to a persistent problem in clinical speech processing. The iterative LLM architecture, with alternating speaker and word recognition passes, represents a significant advancement over conventional linear post-processing models. The choice to evaluate across two distinct clinical settings—each with unique linguistic demands—demonstrates methodological rigor. The statistical validation via Wilcoxon signed-rank tests adds credibility to the findings. Importantly, the reported computational efficiency (RTF 0.32) is a critical factor for real-world clinical adoption. While the study’s scope is narrow, the underlying architecture is sufficiently flexible to be adapted to other languages or contexts. The absence of output failures and the modest computational overhead further suggest practicality. This work bridges a tangible gap between academic innovation and applied clinical needs, offering a replicable template for similar efforts in multilingual medical transcription.
Recommendations
- ✓ 1. Extend validation to additional French clinical domains beyond the two tested datasets to assess broader applicability.
- ✓ 2. Explore integration of the LLM post-processing pipeline into existing clinical transcription platforms as a modular component for incremental adoption.