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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., (©Emilia Ambrosini, Chiara Giangregorio, Eugenio Lomurno, Sara Moccia, Marios Milis, Christos Loizou, Domenico Azzolino, Matteo Cesari, Manuel Cid Gala, Carmen Galán de Isla, Jonathan Gomez-Raja, Nunzio Alberto Borghese, Matteo Matteucci, Simona Ferrante. Originally published in JMIR Aging (https://aging.jmir.org), 29.04.2024.)