Accent related errors in clinical speech transcription and a LLM-based remedy.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41772044.
- Also identified by DOI 10.1038/s41746-026-02490-z and PMC identifier 13066590.
- Licence recorded as CC BY-NC-ND.
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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.