Automatic spontaneous speech analysis for the detection of cognitive functional decline in the elderly: a multi-language study.

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 elderly people. OBJECTIVE:This work aimed to exploit automatic analysis of free speech to identify signs of cognitive function decline. METHODS:A sample of 266 subjects aged over 65 years were recruited in Italy and Spain and were divided into three groups according to their Mini-Mental Status Examination (MMSE) score. 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 multi-class classification problems using both mono- and cross-lingual approaches. The algorithms were enriched using SHAP for model explainability. RESULTS:On the Italian dataset, healthy subjects (MMSE≥27) were automatically discriminated from subjects with a mildly impaired cognitive function (20≤MMSE≤26) and from those with a moderate to severe impairment of cognitive function (11≤MMSE≤19) with an accuracy of 80% and 86%, respectively. Slightly lower performance was achieved on the Spanish and multi-language datasets. CONCLUSIONS:This work proposed a transparent and unobtrusive assessment method, which might be included in a mobile app for large-scale monitoring of cognitive functionality in elderly people. Voice is confirmed to be an important biomarker of cognitive decline due to its non-invasive and easily accessible nature. CLINICALTRIAL: