Repository logo
 

Deep learning-based automated speech detection as a marker of social functioning in late-life depression.

Accepted version
Peer-reviewed

No Thumbnail Available

Type

Article

Change log

Authors

Alshabrawy, Ossama 
Stow, Daniel 
Ferrier, I Nicol 
McNaney, Roisin 

Abstract

BACKGROUND: Late-life depression (LLD) is associated with poor social functioning. However, previous research uses bias-prone self-report scales to measure social functioning and a more objective measure is lacking. We tested a novel wearable device to measure speech that participants encounter as an indicator of social interaction. METHODS: Twenty nine participants with LLD and 29 age-matched controls wore a wrist-worn device continuously for seven days, which recorded their acoustic environment. Acoustic data were automatically analysed using deep learning models that had been developed and validated on an independent speech dataset. Total speech activity and the proportion of speech produced by the device wearer were both detected whilst maintaining participants' privacy. Participants underwent a neuropsychological test battery and clinical and self-report scales to measure severity of depression, general and social functioning. RESULTS: Compared to controls, participants with LLD showed poorer self-reported social and general functioning. Total speech activity was much lower for participants with LLD than controls, with no overlap between groups. The proportion of speech produced by the participants was smaller for LLD than controls. In LLD, both speech measures correlated with attention and psychomotor speed performance but not with depression severity or self-reported social functioning. CONCLUSIONS: Using this device, LLD was associated with lower levels of speech than controls and speech activity was related to psychomotor retardation. We have demonstrated that speech activity measured by wearable technology differentiated LLD from controls with high precision and, in this study, provided an objective measure of an aspect of real-world social functioning in LLD.

Description

Keywords

Ageing, deep learning, late-life depression, social functioning, speech, wearable technology, Aged, Aged, 80 and over, Aging, Attention, Case-Control Studies, Deep Learning, Depressive Disorder, Major, England, Female, Humans, Male, Neuropsychological Tests, Social Adjustment, Social Interaction, Speech, Wearable Electronic Devices

Journal Title

Psychol Med

Conference Name

Journal ISSN

0033-2917
1469-8978

Volume Title

51

Publisher

Cambridge University Press (CUP)

Rights

All rights reserved