解码差异:评估自动语音识别系统在转录黑白患者与家庭医疗保健护士的口头沟通中的表现
Maryam Zolnoori1,2,3, Sasha Vergez3, Zidu Xu2
1Columbia University Irving Medical Center, New York, NY 10032, United States.
JAMIA open
|December 11, 2024
概括
自动语音识别 (ASR) 系统在家庭医疗保健中显示了准确性差异,黑人患者的表现更差. 提高ASR公平性对于不同患者群体的风险预测至关重要.
科学领域:
- 人工智能的人工智能
- 语音处理 语音处理
- 医疗保健技术 技术 医疗保健 技术
背景情况:
- 将人工智能 (AI) 和自动语音识别 (ASR) 整合到家庭医疗保健 (HHC) 中是一个不断增长的趋势.
- 患者与护士沟通的音频记录可以增强风险识别模型.
- 评估ASR转录的准确性和公平性对于可靠的HHC应用是必不可少的.
研究的目的:
- 评估四个ASR系统 (AWS General,AWS Medical,Whisper,Wave2Vec) 的转录准确性和种族公平性,用于美国HHC患者与护士的互动.
- 专注于黑人和白人英语患者的语音转录的表现差异.
- 识别特定的语言挑战和ASR表现中的差异.
主要方法:
- 分析了纽约市35名HHC患者 (16名黑人,19名白人) 的860个录音发言.
- 使用文字错误率 (WER) 对手册黄金标准进行ASR性能测量.
- 使用语言调查和词数 (LIWC) 工具评估种族差异,并分析填充词和固有名词等语音元素.
主要成果:
- 亚马逊网络服务 (AWS) 通用实现了最高的整体准确性 (WER中位数为39%).
- 所有测试的ASR系统都显示黑人患者的准确性降低,LIWC尺寸有显著差异.
- 对于医疗术语,AWS医疗表现更好,而所有系统都在填充词和非医疗术语方面扎.
结论:
- 虽然AWS系统表现出卓越的准确性,但显著的种族差异需要更多样化的训练数据和改进的方言灵敏度.
- 解决ASR性能差异对于HHC环境中的公平应用至关重要.
- 增强的ASR股权将提高利用音频记录的HC通信的风险预测模型的可靠性.
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