BACKGROUND There is a need for fast, accessible, low-cost, and accurate diagnostic methods for early detection of cognitive decline. Dementia diagnoses are usually made years after symptom onset, missing a window of opportunity for early intervention. OBJECTIVE To evaluate the use of recorded voice features as proxies for cognitive function by using neuropsychological test measures and existing dementia diagnoses. METHODS This study analyzed 170 audio recordings, transcripts, and paired neuropsychological test results from 135 participants selected from the Framingham Heart Study (FHS), which includes 97 recordings of cognitively normal participants and 73 recordings of cognitively impaired participants. Acoustic and linguistic features of the voice samples were correlated with cognitive performance measures to verify their association. RESULTS Language and voice features, when combined with demographic variables, performed with an AUC of 0.942 (95% CI 0.929-0.983) in predicting cognitive status. Features with good predictive power included the acoustic features mean spectral slope in the 500-1500 Hz band, variation in the F2 bandwidth, and variation in the Mel-Frequency Cepstral Coefficient (MFCC) 1; the demographic features employment, education, and age; and the text features of number of words, number of compound words, number of unique nouns, and number of proper names. CONCLUSION Several linguistic and acoustic biomarkers show correlations and predictive power with regard to neuropsychological testing results and cognitive impairment diagnoses, including dementia. This initial study paves the way for a follow-up comprehensive study incorporating the entire FHS cohort.

中文翻译：

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在弗雷明汉心脏研究认知老化队列数据中，评估语言和语音生物标记物的功能以预测认知障碍。

背景技术需要用于早期发现认知下降的快速，可访问，低成本和准确的诊断方法。痴呆症的诊断通常是在症状发作数年后进行的，因此缺少早期干预的机会之窗。目的通过使用神经心理学测试方法和现有的痴呆症诊断方法来评估所记录的语音特征作为认知功能的代理。方法本研究分析了来自Framingham心脏研究（FHS）的135名参与者的170项录音，笔录和配对的神经心理学测试结果，其中包括97项认知正常参与者的录音和73项认知障碍参与者的录音。语音样本的声学和语言特征与认知表现指标相关联，以验证其关联性。结果语言和语音功能与人口统计变量结合后，在预测认知状态时的AUC为0.942（95％CI 0.929-0.983）。具有良好预测能力的特征包括声学特征，500-1500 Hz频带中的频谱斜率，F2带宽的变化以及梅尔频率倒谱系数（MFCC）1的变化；就业，受教育程度和年龄的人口特征；以及字数，复合词数，唯一名词数和专有名词数的文本特征。结论几种语言和声学生物标志物在神经心理学测试结果和认知障碍诊断（包括痴呆）方面显示出相关性和预测能力。这项初步研究为纳入整个FHS队列的后续综合研究铺平了道路。