Feb. 13, 2024, 5:49 a.m. | Ayo Adedeji Sarita Joshi Brendan Doohan

cs.CL updates on arXiv.org arxiv.org

In the rapidly evolving landscape of medical documentation, transcribing clinical dialogues accurately is increasingly paramount. This study explores the potential of Large Language Models (LLMs) to enhance the accuracy of Automatic Speech Recognition (ASR) systems in medical transcription. Utilizing the PriMock57 dataset, which encompasses a diverse range of primary care consultations, we apply advanced LLMs to refine ASR-generated transcripts. Our research is multifaceted, focusing on improvements in general Word Error Rate (WER), Medical Concept WER (MC-WER) for the accurate transcription …

accuracy asr automatic speech recognition clinical cs.cl cs.sd dataset diverse documentation eess.as healthcare landscape language language models large language large language models llms medical recognition sound speech speech recognition study systems transcription

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