@Article{info:doi/10.2196/50537,
author="Ambrosini, Emilia
and Giangregorio, Chiara
and Lomurno, Eugenio
and Moccia, Sara
and Milis, Marios
and Loizou, Christos
and Azzolino, Domenico
and Cesari, Matteo
and Cid Gala, Manuel
and Gal{\'a}n de Isla, Carmen
and Gomez-Raja, Jonathan
and Borghese, Nunzio Alberto
and Matteucci, Matteo
and Ferrante, Simona",
title="Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study",
journal="JMIR Aging",
year="2024",
month="Apr",
day="29",
volume="7",
pages="e50537",
keywords="cognitive decline; speech processing; machine learning; multilanguage; Mini-Mental Status Examination",
abstract="Background: The rise in life expectancy is associated with an increase in long-term and gradual cognitive decline. Treatment effectiveness is enhanced at the early stage of the disease. Therefore, there is a need to find low-cost and ecological solutions for mass screening of community-dwelling older adults. Objective: This work aims to exploit automatic analysis of free speech to identify signs of cognitive function decline. Methods: A sample of 266 participants older than 65 years were recruited in Italy and Spain and were divided into 3 groups according to their Mini-Mental Status Examination (MMSE) scores. People were asked to tell a story and describe a picture, and voice recordings were used to extract high-level features on different time scales automatically. Based on these features, machine learning algorithms were trained to solve binary and multiclass classification problems by using both mono- and cross-lingual approaches. The algorithms were enriched using Shapley Additive Explanations for model explainability. Results: In the Italian data set, healthy participants (MMSE score≥27) were automatically discriminated from participants with mildly impaired cognitive function (20≤MMSE score≤26) and from those with moderate to severe impairment of cognitive function (11≤MMSE score≤19) with accuracy of 80{\%} and 86{\%}, respectively. Slightly lower performance was achieved in the Spanish and multilanguage data sets. Conclusions: This work proposes a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in older adults. Voice is confirmed to be an important biomarker of cognitive decline due to its noninvasive and easily accessible nature. ",
issn="2561-7605",
doi="10.2196/50537",
url="https://aging.jmir.org/2024/1/e50537",
url="https://doi.org/10.2196/50537",
url="http://www.ncbi.nlm.nih.gov/pubmed/38386279"
}