Detection of Early Alzheimer’s Disease Using Vocal Biomarkers in Latin American Spanish

Researchers from Canary Speech, in collaboration with Asociación Grupo Ermita Alzheimer de Guatemala, evaluated vocal biomarkers for Alzheimer's disease detection using a new Latin American Spanish dataset collected in Guatemala.

The study compared multiple machine-learning approaches using audio-only data and found that the best-performing model achieved an unweighted average recall of 0.82, with 0.81 sensitivity and 0.83 specificity. The researchers also evaluated prediction consistency under simulated real-world recording conditions, including background noise, changes in microphone characteristics, room acoustics, and loudness. Across these conditions, the model produced the same predicted classification in 90-98% of cases.

The findings contribute to research on vocal biomarkers beyond English-language datasets and provide additional insight into the reliability of audio-based approaches under varied recording conditions.

Reference: Raymond Brueckner, Namhee Kwon, Vinod Subramanian, Rachel Noack, Nate Blaylock, Henry. "Detection of Early Alzheimer's Disease Using Vocal Biomarkers in Latin American Spanish", Proceedings of IberSPEECH 2026, November, 2026.