Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization
arXiv:2603.00086v2 Announce Type: replace-cross 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, p
The continuous improvement in large language models and speech recognition technologies makes specialized clinical applications feasible, addressing a long-standing challenge in healthcare.
Improved transcription accuracy in medical conversations can significantly enhance patient care, clinical research, and operational efficiency in healthcare settings.
The ability to accurately transcribe and diarize spontaneous clinical speech using LLMs opens new possibilities for automating medical documentation and analysis.
- · Healthcare providers
- · Medical AI companies
- · Patients
- · LLM developers
- · Manual medical transcription services
More accurate and efficient medical record-keeping.
Accelerated development of AI tools for clinical decision support and personalized medicine.
Enhanced regulatory scrutiny on AI in healthcare and new ethical considerations regarding patient data privacy and algorithmic bias.
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Read at arXiv cs.AI