Lexical Speech Features of Spontaneous Speech in Older Persons With and Without Cognitive Impairment: Reliability Analysis.
Lexical Speech Features of Spontaneous Speech in Older Persons With and Without Cognitive Impairment: Reliability Analysis.
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语音分析数据是早期检测阿尔茨海默病的有前途的数字生物标志物。然而，尽管它很重要，但这一领域的研究很少检查老年人是否会产生具有足够一致的特征的自发言语，以用作认知状态的代理标记。本初步研究旨在探讨有认知障碍和无认知障碍的老年人言语词汇特征的一致性。从一项更大的、正在进行的研究中共纳入39名老年人（平均年龄81.1岁，标准差5.9岁）。参与者完成了神经心理测试、图片描述任务和说明文任务，以引出说话。在≥2项认知测试中t得分≤40的参与者被归类为轻度认知障碍（MCI）。使用Python和自然语言工具包自动计算语音特征。在有MCI和没有MCI的人中，基于图像描述任务和解释任务平均相关性的信度指数相似（任务内的r范围为0.49至0.65）。个体内部的变异在词汇语音特征中普遍存在。言语率和填充率是认知完整组最一致的指标，言语率在轻度认知障碍组最一致。我们的研究结果表明，自动计算的言语词汇特性在不同程度认知障碍的老年人中是一致的。这些发现鼓励进一步研究语音分析和其他数字生物标志物在监测认知状态方面的效用。
Speech analysis data are promising digital biomarkers for the early detection of Alzheimer disease. However, despite its importance, very few studies in this area have examined whether older adults produce spontaneous speech with characteristics that are sufficiently consistent to be used as proxy markers of cognitive status. This preliminary study seeks to investigate consistency across lexical characteristics of speech in older adults with and without cognitive impairment. A total of 39 older adults from a larger, ongoing study (age: mean 81.1, SD 5.9 years) were included. Participants completed neuropsychological testing and both picture description tasks and expository tasks to elicit speech. Participants with T-scores of ≤40 on ≥2 cognitive tests were categorized as having mild cognitive impairment (MCI). Speech features were computed automatically by using Python and the Natural Language Toolkit. Reliability indices based on mean correlations for picture description tasks and expository tasks were similar in persons with and without MCI (with r ranging from 0.49 to 0.65 within tasks). Intraindividual variability was generally preserved across lexical speech features. Speech rate and filler rate were the most consistent indices for the cognitively intact group, and speech rate was the most consistent for the MCI group. Our findings suggest that automatically calculated lexical properties of speech are consistent in older adults with varying levels of cognitive impairment. These findings encourage further investigation of the utility of speech analysis and other digital biomarkers for monitoring cognitive status over time.