Pérez-Toro, PA;
Dineley, J;
Kaczkowska, A;
Conde, P;
Zhang, Y;
Matcham, F;
Siddi, S;
... Cummins, N; + view all
(2024)
Longitudinal Modeling of Depression Shifts Using Speech and Language.
In:
ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).
(pp. pp. 12021-12025).
IEEE: Seoul, Korea, Republic of.
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Abstract
Speech analysis can provide a potential non-invasive and objective means of assessing and monitoring an individual's mental health. Most studies to date have focused on cross-sectional analysis and have not explored the benefits of speech analysis as a longitudinal monitoring tool that can assist in the management of chronic conditions such as major depressive disorder (MDD). Objectively monitoring for shifts in depression symptom severity levels over time presents a notable challenge, which we address through an automated approach using longitudinal English and Spanish speech samples collected from a clinical population. We employ time-frequency representations and linguistic embeddings to enhance the early recognition of alterations in depression levels in individuals with MDD. We investigate the suitability of using siamese-based training for modeling these changes, intending to enable personalized and adaptive interventions.
Type: | Proceedings paper |
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Title: | Longitudinal Modeling of Depression Shifts Using Speech and Language |
Event: | ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) |
Dates: | 14 Apr 2024 - 19 Apr 2024 |
Open access status: | An open access version is available from UCL Discovery |
DOI: | 10.1109/ICASSP48485.2024.10447195 |
Publisher version: | http://dx.doi.org/10.1109/icassp48485.2024.1044719... |
Language: | English |
Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions. |
Keywords: | Training, Adaptation models, Speech analysis, Sociology, Mental health, Signal processing, Depression |
UCL classification: | UCL UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics UCL > Provost and Vice Provost Offices > School of Life and Medical Sciences > Faculty of Population Health Sciences > Institute of Health Informatics > Clinical Epidemiology |
URI: | https://discovery.ucl.ac.uk/id/eprint/10193575 |
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