论文标题
数据驱动的方法是区分抑郁症和痴呆与嘈杂的语音和语言数据
Data-driven Approach to Differentiating between Depression and Dementia from Noisy Speech and Language Data
论文作者
论文摘要
大量的研究将人类言语的声学和语言特征应用于痴呆和抑郁症的突出标志。但是，很少见到有关痴呆症抑郁症的研究。痴呆症的合并抑郁症经常出现，这些临床疾病具有许多重叠的症状，但是区分抑郁症和痴呆症的能力是必不可少的，因为抑郁症通常是可以治愈的。在这项工作中，我们研究了聚类方法在区分抑郁症和痴呆症与人类言语的能力。我们介绍了一个新颖的汇总数据集，该数据集结合了来自多种疾病的叙事语音数据，即阿尔茨海默氏病，轻度认知障碍，健康控制和抑郁症。我们比较线性和非线性聚类方法，并表明非线性聚类技术可以更好地区分不同的疾病簇。我们的可解释性分析表明，痴呆和抑郁症之间的主要区别症状是语音的声学异常，重复性（或循环），单词发现难度，连贯性障碍以及词汇复杂性和丰富性的差异。
A significant number of studies apply acoustic and linguistic characteristics of human speech as prominent markers of dementia and depression. However, studies on discriminating depression from dementia are rare. Co-morbid depression is frequent in dementia and these clinical conditions share many overlapping symptoms, but the ability to distinguish between depression and dementia is essential as depression is often curable. In this work, we investigate the ability of clustering approaches in distinguishing between depression and dementia from human speech. We introduce a novel aggregated dataset, which combines narrative speech data from multiple conditions, i.e., Alzheimer's disease, mild cognitive impairment, healthy control, and depression. We compare linear and non-linear clustering approaches and show that non-linear clustering techniques distinguish better between distinct disease clusters. Our interpretability analysis shows that the main differentiating symptoms between dementia and depression are acoustic abnormality, repetitiveness (or circularity) of speech, word finding difficulty, coherence impairment, and differences in lexical complexity and richness.
