TY  - JOUR
AU  - Ambrosini, Emilia
AU  - Giangregorio, Chiara
AU  - Lomurno, Eugenio
AU  - Moccia, Sara
AU  - Milis, Marios
AU  - Loizou, Christos
AU  - Azzolino, Domenico
AU  - Cesari, Matteo
AU  - Cid Gala, Manuel
AU  - Galán de Isla, Carmen
AU  - Gomez-Raja, Jonathan
AU  - Borghese, Nunzio Alberto
AU  - Matteucci, Matteo
AU  - Ferrante, Simona
PY  - 2024
DA  - 2024/4/29
TI  - Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study
JO  - JMIR Aging
SP  - e50537
VL  - 7
KW  - cognitive decline
KW  - speech processing
KW  - machine learning
KW  - multilanguage
KW  - Mini-Mental Status Examination
AB  - 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. 
SN  - 2561-7605
UR  - https://aging.jmir.org/2024/1/e50537
UR  - https://doi.org/10.2196/50537
UR  - http://www.ncbi.nlm.nih.gov/pubmed/38386279
DO  - 10.2196/50537
ID  - info:doi/10.2196/50537
ER  -