Acoustic Biomarkers for Alzheimer's Disease Detection from Spontaneous Speech
The study tested if acoustic features extracted from spontaneous speech can reliably distinguish Alzheimer's disease patients from healthy controls using machine learning. By analyzing prosody, spectral, and temporal characteristics from a picture-description task dataset, the researchers found that these acoustic biomarkers carry strong discriminative value for diagnosis. This supports using low-cost audio screening tools for dementia risk detection.