Automatic Speech Recognition (ASR) technology is fundamental in transcribing
spoken language into text, with considerable applications in the clinical
realm, including streamlining medical transcription and integrating with
Electronic Health Record (EHR) systems. Nevertheless, challenges persist,
especially when transcriptions contain noise, leading to significant drops in
performance when Natural Language Processing (NLP) models are applied. Named
Entity Recognition (NER), an essential clinical task, is particularly affected
by such noise, often termed the ASR-NLP gap. Prior works have primarily studied
ASR's efficiency in clean recordings, leaving a research gap concerning the
performance in noisy environments. This paper introduces a novel dataset,
BioASR-NER, designed to bridge the ASR-NLP gap in the biomedical domain,
focusing on extracting adverse drug reactions and mentions of entities from the
Brief Test of Adult Cognition by Telephone (BTACT) exam. Our dataset offers a
comprehensive collection of almost 2,000 clean and noisy recordings. In
addressing the noise challenge, we present an innovative transcript-cleaning
method using GPT4, investigating both zero-shot and few-shot methodologies. Our
study further delves into an error analysis, shedding light on the types of
errors in transcription software, corrections by GPT4, and the challenges GPT4
faces. This paper aims to foster improved understanding and potential solutions
for the ASR-NLP gap, ultimately supporting enhanced healthcare documentation
practices.

自动语音识别（ASR）技术是将口语转录为文本的基本技术，广泛应用于临床领域，包括简化医学转录和与电子健康记录（EHR）系统集成。然而，ASR-NLP 领域仍存在挑战，尤其是当转录中含有噪音时，导致自然语言处理（NLP）模型的性能显著下降。本论文介绍了一个新颖的数据集，BioASR-NER，旨在填补生物医学领域 ASR-NLP 差距，重点提取不良药物反应和 Brief Test of Adult Cognition by Telephone（BTACT）考试中实体的提及。我们的数据集提供了近 2000 个干净和有噪音的录音。针对噪音挑战，我们提出了一种使用 GPT4 的创新的文本清理方法，并研究了零样本和少样本方法。我们的研究进一步进行了错误分析，揭示了转录软件中的错误类型、GPT4 的纠正和 GPT4 面临的挑战。本论文旨在促进对 ASR-NLP 差距的更好理解和潜在解决方案，最终支持改进的医疗文档化实践。