← Back to all news
arXiv AI By Woori Jeon, Seunghee Ha, Sang-Kyu Lee, Ji Hye Yoon, Tae-Jin Yoon, Seung Jin Lee, Woojae Han, Jungmin So

Disentangling Depression from Cognitive Decline in Elderly Speech Using Concurrent Clinical Assessments

Read the original on arXiv AI →

The study presents a Korean elderly speech corpus of 89 MCI speakers collected over three years, accompanied by concurrent depression (SGDS) and cognitive (MMSE) assessments. Analysis using linear mixed-effects models and partial correlations revealed that only formant features are associated with depression after controlling for cognitive function, while F0 features show no signal. A subset of formant features (F1+F2) achieved a UAR of 0.760 with just 12 features, outperforming both the full eGeMAPS baseline and self-supervised representations.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

Related stories

arXiv Computation and Language
Aug 28

Towards Interpretable Depression Detection: Linking Acoustic Features to DSM-5 Indicators

The paper proposes a transparent framework that links speech acoustic features—such as pitch variability, pauses, and speech tempo—to DSM‑5 indicators of depression, offering interpretable, indicator‑level outputs instead of opaque black‑box models. It runs locally on commodity hardware to preserve privacy and has been preliminarily evaluated on the DAIC‑WOZ dataset, showing consistent associations between acoustic cues and DSM‑5 indicators of psychomotor change and concentration difficulty. Future work aims to validate the approach on longitudinal data and expand multimodal integration while keeping edge constraints.

By Jonas L\"anzlinger, Katharina O. E. M\"uller, Burkhard Stiller, Bruno Rodrigues
multimodalsafety
More like this →
arXiv Machine Learning
Oct 12

Acoustic Biomarkers for Alzheimer's Disease Detection from Spontaneous Speech

The study explores acoustic biomarkers derived from spontaneous speech to differentiate Alzheimer's disease patients from healthy controls. Using a structured picture‑description task, low‑level prosodic, spectral, and temporal features were extracted and fed into various machine learning classifiers. The best models achieved clinically meaningful separation between diagnostic groups, indicating that non‑invasive audio screening could aid early detection of dementia.

By Anh L Tran, Arjee Javellana Restar, Bereket Kindo
multimodal
More like this →
arXiv Machine Learning
Aug 31

Multilingual Lexical Feature Analysis of Spoken Language for Predicting Major Depression Symptom Severity

arXiv:2511.07011v2 Announce Type: replace-cross Abstract: Background: Remotely captured spoken language could provide objective, regular indicators of depression symptom severity. However, research t...

By Anastasiia Tokareva, Judith Dineley, Zoe Firth, Pauline Conde, Faith Matcham, Sara Siddi, Femke Lamers, Ewan Carr, Carolin Oetzmann, Daniel Leightley, Yuezhou Zhang, Amos A. Folarin, Josep Maria Haro, Brenda W. J. H. Penninx, Raquel Bailon, Srinivasan Vairavan, Til Wykes, Richard J. B. Dobson, Vaibhav A. Narayan, Matthew Hotopf, Nicholas Cummins, The RADAR-CNS Consortium
ragnlpsafety
More like this →