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Detecting Apathy in Older Adults with Cognitive Disorders Using Automatic Speech Analysis.
Alexandra König1,2, Nicklas Linz3, Radia Zeghari1
1CoBTeK (Cognition-Behaviour-Technology) Lab, Memory Center CHU, Université Côte d'Azur, Nice, France.
Journal of Alzheimer'S Disease : JAD
|May 26, 2019
Summary
Automatic speech analysis shows promise for detecting apathy, a condition affecting psychiatric and neurological disorders. Temporal speech features were most effective in distinguishing apathetic individuals, with machine learning models achieving high accuracy.
Area of Science:
- Neurology
- Psychiatry
- Computational Linguistics
- Biomedical Engineering
Background:
- Apathy negatively impacts disease progression in psychiatric and neurological conditions.
- Apathy is a potential predictor of increased dementia risk in older adults.
- Current apathy assessment methods lack objectivity and sensitivity, necessitating novel diagnostic tools.
Purpose of the Study:
- To investigate the potential of automatic speech analysis for characterizing and detecting apathy.
- To explore speech as a potential objective biomarker for apathy assessment.
Main Methods:
- Sixty patients with mild to moderate neurocognitive disorder (apathetic and non-apathetic groups) participated.
- Automatic extraction and analysis of paralinguistic speech markers (prosodic, formant, source, temporal).
- Machine learning models were employed to validate the diagnostic power of speech features.
Main Results:
- Temporal speech aspects correlated with reduced interest and initiative in apathy.
- Prosody features related to the affective domain of apathy.
- Machine learning models achieved high diagnostic performance (AUC up to 0.88 for males, 0.77 for females).
Conclusions:
- Speech analysis is a viable and reliable biomarker for apathy detection and assessment.
- Automatic speech analysis offers a sensitive and objective method for evaluating apathy.
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