Neurodegenerative conditions like Alzheimer disease affect millions and have no known cure, making early detection important. In addition to memory impairments, dementia causes substantial changes in speech production, particularly lexical-semantic characteristics. Existing clinical tools for detecting change often require considerable expertise or time, and efficient methods for identifying persons at risk are needed. This study examined whether early stages of cognitive decline can be identified using an automated calculation of lexical-semantic features of participants’ spontaneous speech. Unimpaired or mildly impaired older adults (N = 39, mean 81 years old) produced several monologues (picture descriptions and expository descriptions) and completed a neuropsychological battery, including the Modified Mini-Mental State Exam. Most participants (N = 30) returned one year later for follow-up. Lexical-semantic features of participants’ speech (particularly lexical frequency) were significantly correlated with cognitive status at the same visit and also with cognitive status one year in the future. Thus, automated analysis of speech production is closely associated with current and future cognitive test performance and could provide a novel, scalable method for longitudinal tracking of cognitive health.

像阿尔茨海默病这样的神经退行性疾病影响了数百万人，并且没有已知的治愈方法，因此早期发现很重要。除了记忆障碍之外，痴呆症还会导致言语产生的重大变化，尤其是词汇语义特征。用于检测变化的现有临床工具通常需要大量的专业知识或时间，并且需要识别处于危险中的人的有效方法。这项研究检查了是否可以使用参与者自发语音的词汇语义特征的自动计算来识别认知能力下降的早期阶段。未受损或轻度受损的老年人（N = 39，平均 81 岁）进行了几次独白（图片描述和说明性描述）并完成了神经心理学测试，包括修改后的简易精神状态检查。大多数参与者（N = 30）一年后返回进行随访。参与者言语的词汇语义特征（特别是词汇频率）与同一访问时的认知状态以及未来一年的认知状态显着相关。因此，语音生成的自动分析与当前和未来的认知测试性能密切相关，并且可以为认知健康的纵向跟踪提供一种新颖的、可扩展的方法。