arXiv cs.CL· Woori Jeon, Seunghee Ha, Sang-Kyu Lee, Ji Hye Yoon, Tae-Jin Yoon, Seung Jin Lee, Woojae Han, Jungmin So·· 12 小时前AI 评分26
利用同步临床评估区分老年语音中的抑郁与认知衰退
Disentangling Depression from Cognitive Decline in Elderly Speech Using Concurrent Clinical Assessments
AI 导读
研究构建了89名MCI老年说话人的韩语语音语料库，采集历时三年并同步进行抑郁（SGDS）与认知（MMSE）评估。在控制认知功能后，七类特征组中仅共振峰特征与抑郁相关，常用的F0特征无信号；仅用12个特征的F1+F2子集达到UAR 0.760，优于完整eGeMAPS基线和自监督表征。
正文 · 原文
Abstract:Detecting depression from speech in elderly patients with mild cognitive impairment (MCI) is complicated by overlapping acoustic effects of cognitive decline. Without disentangling these, classifiers risk learning cognitive rather than depression-specific patterns. We present a Korean elderly speech corpus of 89 MCI speakers collected over three years with concurrent depression (SGDS) and cognitive (MMSE) assessments. Both linear mixed-effects models and partial correlations identify the same pattern: of seven feature groups, only formant features are associated with depression after controlling for cognitive function, while widely used F0 features carry no signal. The F1+F2 subset achieves UAR 0.760 with just 12 features, outperforming both the full eGeMAPS baseline and self-supervised representations. These findings show that concurrent clinical assessments can identify interpretable, depression-specific acoustic markers in populations where cognitive and affective symptoms co-occur.
| Comments: | 6 pages, 5 tables, accepted to Interspeech 2026 |
| Subjects: | Audio and Speech Processing (eess.AS); Artificial Intelligence (cs.AI); Computation and Language (cs.CL); Sound (cs.SD) |
| Cite as: | arXiv:2610.26890 [eess.AS] |
| (or arXiv:2610.26890v1 [eess.AS] for this version) | |
| https://doi.org/10.48550/arXiv.2610.26890 arXiv-issued DOI via DataCite |
Submission history
From: Woori Jeon [view email]
[v1]
Thu, 15 Oct 2026 04:06:55 UTC (34 KB)
来源：arXiv cs.CL · arxiv.org