MeDial-Speech provides 111+ hours of spoken medical dialogues from robot-patient and doctor-patient interactions across four conditions, with a 20-option sentence selection benchmark where Claude Sonnet 4 reaches 71-75% accuracy.
The sound of healthcare: Improving medical transcription asr accuracy with large language models
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
roles
background 1polarities
background 1representative citing papers
A multi-pass LLM architecture alternating speaker and word recognition passes yields significant WDER reductions on French suicide prevention conversations while remaining stable on neurosurgery consultations using Qwen3-Next-80B.
A survey that classifies non-intrusive ASR refinement methods into five categories, reviews domain adaptation and evaluation datasets, proposes standardized metrics, and identifies future research directions.
citing papers explorer
-
A Dataset of Robot-Patient and Doctor-Patient Medical Dialogues for Spoken Language Processing Tasks
MeDial-Speech provides 111+ hours of spoken medical dialogues from robot-patient and doctor-patient interactions across four conditions, with a 20-option sentence selection benchmark where Claude Sonnet 4 reaches 71-75% accuracy.
-
Iterative LLM-based improvement for French Clinical Interview Transcription and Speaker Diarization
A multi-pass LLM architecture alternating speaker and word recognition passes yields significant WDER reductions on French suicide prevention conversations while remaining stable on neurosurgery consultations using Qwen3-Next-80B.