使用自然语言处理和机器学习早期识别语言障碍:挑战和新兴方法
Jessica M Lammert1, Angela C Roberts2,3, Ken McRae4,5
1Graduate Program in Psychology, University of Western Ontario, London, Canada.
Journal of speech, language, and hearing research : JSLHR
|January 9, 2025
概括
人工智能 (AI) 提供了对叙事语言的自动分析,以提高儿童语言障碍的诊断和治疗的效率和可访问性. 这项技术有助于识别发育性语言障碍,并分析儿童语言样本.
科学领域:
- 计算语言学 计算语言学
- 发展心理学 发展心理学
- 医疗保健中的人工智能
背景情况:
- 传统的语言样本分析面临临临床障碍,阻碍了有效的评估.
- 叙事语言采样提供了丰富的数据,但需要大量的手工处理.
- 自动化方法正在出现,以克服儿童语言分析中的这些局限性.
研究的目的:
- 审查计算机化方法来分析儿童的叙事语言.
- 探索使用人工智能和机器学习识别语言障碍.
- 突出自动化工具在提高语言评估效率和可访问性方面的潜力.
主要方法:
- 审查最近关于自然语言处理 (NLP) 和机器学习 (ML) 语言特征提取的研究.
- 解释AI工具如何在分析儿童语言样本中运行.
- 讨论设计决策对这些自动化工具性能的影响.
主要成果:
- 通过NLP和ML证明了语言特征的自动提取.
- 使用这些先进的计算技术,可以识别发育性语言障碍.
- 人工智能工具在分析儿童语言样本复杂特征方面表现有前途.
结论:
- 计算机自动化方法代表了语言样本分析的重大进步.
- 这些方法为儿童语言障碍的诊断和治疗提供了更高的效率和可访问性.
- 解决诸如偏见,访问和通用性等挑战对于广泛采用至关重要.
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