  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.
