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

Little, Bethany, Alshabrawy, Ossama, Stow, Daniel, Ferrier, I. Nicol, McNaney, Roisin, Jackson, Daniel G., Ladha, Karim, Ladha, Cassim, Ploetz, Thomas, Bacardit, Jaume, Olivier, Patrick, Gallagher, Peter and O'Brien, John T. (2021) Deep learning-based automated speech detection as a marker of social functioning in late-life depression. Psychological Medicine, 51 (9). pp. 1441-1450. ISSN 0033-2917

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BackgroundLate-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.MethodsTwenty 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.ResultsCompared 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.ConclusionsUsing 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.

Item Type: Article
Additional Information: Funding information: This work was supported by the Medical Research Council (grant number G1001828/1), the EPSRC (Inclusion through the Digital Economy grant number EP/G066019/1) and Northumberland, Tyne and Wear NHS Foundation Trust Research Capability Funding. JOB was supported by the NIHR Cambridge Biomedical Research Centre. OA was supported by the Newton-Mosharafa fund. JB was supported by the Engineering and Physical Sciences Research Council (grant numbers EP/M020576/1, EP/N031962/1).
Uncontrolled Keywords: Ageing; deep learning; late-life depression; social functioning; speech; wearable technology
Subjects: B800 Medical Technology
C800 Psychology
G400 Computer Science
Department: Faculties > Engineering and Environment > Computer and Information Sciences
Depositing User: Elena Carlaw
Date Deposited: 13 Mar 2020 15:56
Last Modified: 06 Aug 2021 15:45

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