发展性语言障碍查的自动化方法:全面审查和未来前景
Yangna Hu1, Cindy Sing Bik Ngai1, Sihui Chen1
1The Department of Chinese and Bilingual Studies, The Hong Kong Polytechnic University, Hung Hom, Kowloon.
Journal of speech, language, and hearing research : JSLHR
|April 14, 2025
概括
本次对发育性语言障碍 (DLD) 自动查的审查发现模型使用各种语言和特征. 未来的研究需要更大的数据集和提高DLD检测的灵敏度.
科学领域:
- 语音语言病理学 语言病理学
- 计算语言学计算语言学
- 机器学习在医疗保健中的应用
背景情况:
- 发育性语言障碍 (DLD) 是一种影响语言的神经发育缺陷.
- 对于DLD的生物医学病因尚未确定.
- 自动选方法为早期的DLD识别提供了潜在的潜力.
研究的目的:
- 系统地审查DLD的自动选方法.
- 分析使用的语言,数据集,特征和分类技术.
- 确定优势,弱点和未来的研究方向.
主要方法:
- 对2024年3月之前发表的研究进行系统文献综述.
- 在PubMed,科学网,Scopus和PsycINFO进行的搜索.
- 纳入标准侧重于儿童的自动化DLD查系统.
主要成果:
- 审查了23项研究,重点是捷克语,意大利语,普通话,西班牙语和英语.
- 通常使用的功能包括声学,纹理,语音和非语音数据.
- 分类方法范围从传统的机器学习到深度学习 (CNN,LSTM).
- 确定了需要更大,多语言数据集和增强系统灵敏性的需求.
结论:
- 在自动DLD查方面取得了重大进展.
- 未来的研究应该专注于整合不同的功能和算法.
- 解决年龄,性别,严重程度和并发症的差异对于改善DLD查至关重要.
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