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对可解释的语言特征进行统计分析,用于在双语语音语中检测MCI
Arezo Shakeri1, Mina Farmanbar1
1Department of Electrical Engineering and Computer Science University of Stavanger Stavanger Norway.
Alzheimer's & dementia (Amsterdam, Netherlands)
|February 12, 2026
概括
早期发现轻度认知障碍 (MCI) 是至关重要的. 这项研究确定了双语演讲中的特定语言特征,可靠地表明英语和中文之间的MCI,为多语言查工具铺平了道路.
科学领域:
- 神经科学是一个神经科学.
- 计算语言学 计算语言学
- 心理语言学 心理语言学
背景情况:
- 早期发现轻度认知障碍 (MCI) 对于及时干预和预防痴呆症至关重要.
- 在双语环境中,可解释的语言标记在MCI检测方面未得到充分研究.
- 双语数据集为识别跨语言认知标记提供了一个独特的机会.
研究的目的:
- 在双语数据集中识别与MCI相关的可解释的语言特征.
- 探索用于MCI检测的语言特异性和语言不可知特征.
- 建立一个跨语言的基础,开发多语言的MCI评估工具.
主要方法:
- 使用了TAUKADIAL挑战数据集与英语和中文图片描述.
- 使用自然语言数据集高效语言特征提取 (ELFEN) 软件包提取了93个语言特征.
- 使用全面手工语言特征 (LFTK) 工具包提取了141个语言不可知特征,并进行了统计分析 (ANOVA,Tukey's HSD).
主要成果:
- 七个ELFEN和33个LFTK特征在诊断组之间显示出显著差异.
- 轻度认知障碍 (MCI) 语言的特点是词汇多样性减少,代词减少,数字和参与词的使用增加,以及两种语言中的句子更长.
- 分析显示了结构和词汇的变化,中国人的回答显示了更大的变化.
结论:
- 确定了与MCI相关的统计学上显著和可解释的语言特征.
- 在英语和中文图片描述中建立了一致的跨语言标记.
- 为创建透明,多语言的MCI查工具提供了经过验证的基础.
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