%0 Journal Article
%@ 2561-7605
%I JMIR Publications
%V 7
%N 
%P e50537
%T Automatic Spontaneous Speech Analysis for the Detection of Cognitive Functional Decline in Older Adults: Multilanguage Cross-Sectional Study
%A Ambrosini,Emilia
%A Giangregorio,Chiara
%A Lomurno,Eugenio
%A Moccia,Sara
%A Milis,Marios
%A Loizou,Christos
%A Azzolino,Domenico
%A Cesari,Matteo
%A Cid Gala,Manuel
%A Galán de Isla,Carmen
%A Gomez-Raja,Jonathan
%A Borghese,Nunzio Alberto
%A Matteucci,Matteo
%A Ferrante,Simona
%+ Department of Electronics, Information and Bioengineering, Politecnico di Milano, Piazza Leonardo da Vinci 32, Milano, 20133, Italy, 39 0223999509, emilia.ambrosini@polimi.it
%K cognitive decline
%K speech processing
%K machine learning
%K multilanguage
%K Mini-Mental Status Examination
%D 2024
%7 29.4.2024
%9 Original Paper
%J JMIR Aging
%G English
%X 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. 
%M 38386279
%R 10.2196/50537
%U https://aging.jmir.org/2024/1/e50537
%U https://doi.org/10.2196/50537
%U http://www.ncbi.nlm.nih.gov/pubmed/38386279