Scholarlink

<p>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.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

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

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年3月2日

查看全文
  • Yasaman Fatapour,
  • Jamil S. Samaan,
  • Nicholas P. Tatonetti,
  • Aditi Kuchi,
  • Apoorva Srinivasan,
  • Sarvenaz Fatapour,
  • Hongyu Liu,
  • Jacob Berkowitz,
  • Kevin Tsang,
  • Michael Ziets,
  • Nadine Friedrich,
  • Nitin Srinivasan,
  • Shehan Thangaratnam,
  • Ryan King,
  • Ryan Czarny,
  • Trini Nguyen,
  • Yee Hui Yeo,
  • Kim Hyunseok,
  • Yi-Te Lee,
  • Nicha Wongjarupong,
  • Arash Abiri

摘要

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.