Detecting Apathy in Older Adults with Cognitive Disorders using Automatic Speech Analysis

Alexandra König, Nicklas Linz, Radia Zeghari, Xenia Klinge, Johannes Tröger, Jan Alexandersson, Philippe Robert

In: George Perry (editor). Journal of Alzheimer's Disease (JAD) 69 4 Pages 1183-1193 IOS Press 2019.


Objective: Apathy is present in several psychiatric and neurological conditions and has been found to have a severe negative effect on disease progression. In older people, it can be a predictor of increased dementia risk. Current assessment methods lack objectivity and sensitivity, thus new diagnostic tools and broad-scale screening technologies are needed. This study is the first of its kind aiming to investigate whether automatic speech analysis could be used for characterisation and detection of apathy. Methods: A group of apathetic and non-apathetic patients (n = 60) with mild to moderate neurocognitive disorder were recorded while performing two short narrative speech tasks. Paralinguistic markers relating to prosodic, formant, source and temporal qualities of speech were automatically extracted, examined between the groups and compared to baseline assessments. Machine learning experiments were carried out to validate the diagnostic power of extracted markers. Results: Correlations between apathy sub-scales and features revealed a relation between temporal aspects of speech and the subdomains of reduction in interest and initiative, as well as between prosody features and the affective domain. Group differences were found to vary for males and females, depending on the task. Differences in temporal aspects of speech were found to be the most consistent difference between apathetic and non-apathetic patients. Machine learning models trained on speech features achieved top performances of AU C = 0.88 for males and AU C = 0.77 for females. Conclusions: These findings reinforce the usability of speech as a reliable bio-marker in the detection and assessment of apathy.

German Research Center for Artificial Intelligence
Deutsches Forschungszentrum für Künstliche Intelligenz