Automated dementia screening enables early detection and intervention, reducing costs to healthcare systems and increasing quality of life for those affected. Depression has shared symptoms with dementia, adding complexity to diagnoses. The research focus so far has been on binary classification of dementia (DEM) and healthy controls (HC) using speech from picture description tests from a single dataset. In this work, we apply established baseline systems to discriminate cognitive impairment in speech from the semantic Verbal Fluency Test and the Boston Naming Test using text, audio and emotion embeddings in a 3-class classification problem (HC vs. MCI vs. DEM). We perform cross-corpus and mixed-corpus experiments on two independently recorded German datasets to investigate generalization to larger populations and different recording conditions. In a detailed error analysis, we look at depression as a secondary diagnosis to understand what our classifiers actually learn.

翻译：自动化痴呆筛查能够实现早期检测与干预，从而降低医疗系统成本并提高患者生活质量。抑郁症与痴呆症存在共病症状，增加了诊断的复杂性。现有研究主要聚焦于基于单一数据集的图片描述测试语音，对痴呆症（DEM）与健康对照组（HC）进行二分类。本研究采用已有的基线系统，通过文本、音频和情感嵌入，在三分类问题（HC vs. MCI vs. DEM）中区分语义流畅性测试和波士顿命名测试语音中的认知障碍。我们在两个独立记录的德语数据集上进行跨语料库和混合语料库实验，以探究模型在更大群体及不同记录条件下的泛化能力。在详细的误差分析中，我们将抑郁症作为次要诊断指标，以理解分类器实际学习的内容。