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Accent related errors in clinical speech transcription and a LLM-based remedy
0
Zitationen
21
Autoren
2026
Jahr
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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Autoren
Institutionen
- Cedars-Sinai Medical Center(US)
- University of Washington Medical Center(US)
- Columbia University(US)
- University of California, Irvine(US)
- University of Southern California(US)
- Martin Luther King, Jr. Multi-Service Ambulatory Care Center(US)
- Martin Luther King, Jr. Community Hospital(US)
- University of California, Irvine Medical Center(US)
- Chonburi Hospital(TH)