What question did this study set out to answer?

This research aims to evaluate the effectiveness of AI tools in improving clinical speech transcription accuracy, particularly for non-native speakers.

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March 4, 2026Open Access

Accent related errors in clinical speech transcription and a LLM-based remedy

Key Points

This research aims to evaluate the effectiveness of AI tools in improving clinical speech transcription accuracy, particularly for non-native speakers.
Tested Whisper and WhisperX on native and non-native English clinical speech.
Measured error rates to assess performance across different accents.
Applied GPT-4o for post-processing to enhance transcription accuracy.
Significantly higher transcription error rates were observed for non-native speakers.
Post-processing with GPT-4o restored accuracy lost during initial transcription.
The combined approach of WhisperX and GPT-4o effectively reduced accent-related errors.

Abstract

Accurate clinical documentation is essential for safe, effective patient care. AI tools powered by automatic speech recognition can streamline this process. Variable performance across speakers with diverse accents leads to transcription errors and clinical risk. In testing Whisper and WhisperX on native and non-native English clinical speech, error rates were significantly higher for non-native speakers. Post-processing with GPT-4o restored lost accuracy. This chained approach (WhisperX-GPT) reduced accent-related errors.

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