Alzheimer's & dementia (Amsterdam, Netherlands) · 2025
Acoustic speech features are associated with late-life depression and apathy symptoms: Preliminary findings.
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Harlev Daniel, Singer Shir, Goldshalger Maya, Wolpe Noham, Bergmann Eyal
Аннотация
BACKGROUND: Late-life depression (LLD) is a heterogenous disorder related to cognitive decline and neurodegenerative processes, raising a need for the development of novel biomarkers. We sought to provide preliminary evidence for acoustic speech signatures sensitive to LLD and their relationship to depressive dimensions.
METHODS: Forty patients (24 female, aged 65-82 years) were assessed with the Geriatric Depression Scale (GDS). Vocal features were extracted from speech samples (reading a pre-written text) and tested as classifiers of LLD using random forest and XGBoost models. Post hoc analyses examined the relationship between these acoustic features and specific depressive dimensions.
RESULTS: The classification models demonstrated moderate discriminative ability for LLD with receiver operating characteristic = 0.78 for random forest and 0.84 for XGBoost in an out-of-sample testing set. The top classifying features were most strongly associated with the apathy dimension (R 2 = 0.43).
DISCUSSION: Acoustic vocal features that may support the diagnosis of LLD are preferentially associated with apathy.
HIGHLIGHTS: The depressive dimensions in late-life depression (LLD) have different cognitive correlates, with apathy characterized by more pronounced cognitive impairment.Acoustic speech features can predict LLD. Using acoustic features, we were able to train a random forest model to predict LLD in a held-out sample.Acoustic speech features that predict LLD are preferentially associated with apathy. These results indicate a predominance of apathy in the vocal signatures of LLD, and suggest that the clinical heterogeneity of LLD should be considered in development of acoustic markers.
METHODS: Forty patients (24 female, aged 65-82 years) were assessed with the Geriatric Depression Scale (GDS). Vocal features were extracted from speech samples (reading a pre-written text) and tested as classifiers of LLD using random forest and XGBoost models. Post hoc analyses examined the relationship between these acoustic features and specific depressive dimensions.
RESULTS: The classification models demonstrated moderate discriminative ability for LLD with receiver operating characteristic = 0.78 for random forest and 0.84 for XGBoost in an out-of-sample testing set. The top classifying features were most strongly associated with the apathy dimension (R 2 = 0.43).
DISCUSSION: Acoustic vocal features that may support the diagnosis of LLD are preferentially associated with apathy.
HIGHLIGHTS: The depressive dimensions in late-life depression (LLD) have different cognitive correlates, with apathy characterized by more pronounced cognitive impairment.Acoustic speech features can predict LLD. Using acoustic features, we were able to train a random forest model to predict LLD in a held-out sample.Acoustic speech features that predict LLD are preferentially associated with apathy. These results indicate a predominance of apathy in the vocal signatures of LLD, and suggest that the clinical heterogeneity of LLD should be considered in development of acoustic markers.