Acoustic Biomarkers for Alzheimer's Disease Detection from Spontaneous Speech
Alzheimer's disease (AD) remains a major public health challenge, and early identification is crucial for timely intervention and care planning. Recent work has shown that speech-based digital biomarkers can capture subtle changes in vocal production, prosody, and timing that often precede or accompany cognitive decline. In this study, we investigate whether acoustic features extracted from spontaneous speech can reliably distinguish individuals with AD from healthy controls. Using a structured speech dataset derived from the Cookie Theft picture-description task, we extract clinically informative low-level descriptors spanning prosodic, spectral, and temporal domains, and evaluate a rang...
| Authors | Anh L Tran, Arjee Javellana Restar, Bereket Kindo |
|---|---|
| Primary category | eess.AS |
| Published | 30 September 2026 |
| Last updated at source | 30 September 2026 |
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