超越单词错误率来评估临床样本中的自动语音识别:精神分裂症谱系障碍研究的教训
Sandra Anna Just1, Brita Elvevåg2, Shrankhla Pandey3
1Department of Clinical Medicine, UiT - The Arctic University of Norway, Tromsø, Norway; Department of Psychiatry and Neurosciences, Campus Charité Mitte, Charité - Universitätsmedizin Berlin, Corporate Member of Freie Universität Berlin and Humboldt Universität zu Berlin, Germany.
Psychiatry research
|August 24, 2025
概括
精神分裂症患者的自动语音识别 (ASR) 性能因症状和起源而异,影响临床NLP工具. 除了文字错误率之外,错误类型和上下文对于在心理健康中安全实施ASR至关重要.
科学领域:
- 计算语言学
- 精神病学
- 医疗保健中的人工智能
背景情况:
- 自动语音识别 (ASR) 对于自然语言处理 (NLP) 在心理健康研究中至关重要,使大规模研究和可扩展的临床工具成为可能.
- 了解临床人群中的ASR性能对于这些技术在医疗机构的安全和有效部署至关重要.
研究的目的:
- 对被诊断患有精神分裂症谱系障碍的个体的语音样本进行ASR评价.
- 确定影响ASR准确性的因素,如症状严重程度和人口变量.
- 评估ASR生成的转录对随后的NLP分析及其与临床测量相关性的影响.
主要方法:
- 分析了50个精神分裂症患者的语音样本.
- 对ASR成绩单的文字错误率 (WER) 的计算.
- 使用NLP指标比较ASR成绩单和手动成绩单,例如GloVe语义相似性和句子数量.
- 从ASR转录和临床症状得分得出的NLP指标之间的相关性评估.
主要成果:
- 词错误率 (WER) 在0. 31至0. 58之间,与出生国家和积极症状严重程度相关的变化.
- 与手动成绩单相比,ASR成绩单具有更高的GloVe语义相似性和更少的句子.
- 从ASR转录获得的NLP指标与手动转录的相比,与临床症状得分的相关性较弱.
结论:
- 临床环境中的ASR性能评估必须超越WER,包括错误类型,含义和上下文.
- 在心理健康研究中,ASR转录的差异可能会影响NLP分析的结果.
- 该研究为评估临床研究中的ASR提供了一个框架,指导电子健康记录,语音聊天机器人和临床决策支持系统的未来开发和实施.
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