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语音启用阅读流性评估:一个验证研究.

Max van der Velde1,2, Wieke Harmsen3, Bernard P Veldkamp1

  • 1Cognition, Data and Education Section, Faculty of BMS, University of Twente, De Zul 10, Enschede, NJ 7522 The Netherlands.

International journal of artificial intelligence in education
|December 11, 2025
PubMed
概括
此摘要是机器生成的。

这项研究表明,自动语音识别 (ASR) 可以准确地评估荷兰语的口语阅读流性,这是一个半透明的语言. 这种基于人工智能的工具为初级教育中测量阅读能力提供了有效和高效的替代方案.

关键词:
评估评估的方法自动化 自动化 自动化流动性 流动性 流动性仪器是指一个工具.阅读 阅读 阅读 阅读验证 验证 验证 验证

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科学领域:

  • 教育技术的教育技术
  • 计算语言学 计算语言学
  • 心理语言学 心理语言学

背景情况:

  • 阅读理解对于社会功能至关重要,但学生的技能在全球范围内正在下降.
  • 口头阅读流 (准确性,速度,自动性,散文) 是阅读理解的先决条件.
  • 目前的流利性评估是繁的,缺乏诊断细节;人工智能提供了潜在的解决方案,但需要验证.

研究的目的:

  • 评估基于自动语音识别 (ASR) 的仪器的有效性,以评估半透明语言的口语阅读流性.
  • 确定ASR是否可以生成有效的文字解码和段落阅读 (准确性,速度,自动性) 的测量.

主要方法:

  • 使用基于论证的验证方法进行验证研究.
  • 分析了来自653名荷兰小学生 (2-3年级) 的176小时的语音数据.
  • 与569个文字阅读和622个段落阅读测试进行比较.

主要成果:

  • 基于ASR的仪器成功生成了荷兰语的有效流度指标 (准确性,速度,自动性).
  • 人工智能方法证明了评估半透明语言的阅读流性的可行性.

结论:

  • 基于ASR的评估是衡量半透明语言口语阅读流性的可行方法.
  • 未来的工作应该集中在优化ASR,错误分析,并将prosody纳入全面评估工具中.