在抑郁存在下的痴呆分类：一项跨语料库研究
Classifying Dementia in the Presence of Depression: A Cross-Corpus Study
AI总结：
本文通过跨语料库实验，利用文本、音频和情感嵌入对语音进行三类分类（HC vs. MCI vs. DEM），探讨抑郁作为次级诊断对分类器的影响。
摘要：
自动痴呆筛查有助于早期检测和干预，减少对 healthcare 系统的成本，提高受影响者的质量生活。抑郁症与痴呆有共享症状，增加了诊断的复杂性。迄今为止，研究重点是使用单个数据集的图片描述测试语音对痴呆（DEM）和健康受试者（HC）进行二分类。在本工作中，我们应用已建立的基线系统，利用语义词汇流畅度测试和波士顿命名测试的语音，通过文本、音频和情感嵌入进行三类分类。我们在两个独立录制的德语数据集上进行跨语料库和混合语料库实验，以研究在更大人群和不同录音条件下的泛化能力。在详细的错误分析中，我们研究抑郁症作为次级诊断，以了解分类器实际上学到了什么。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
