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Classifying depression symptom severity: Assessment of speech representations in personalized and generalized machine learning models

Campbell, Edward L; Dineley, Judith; Conde, Pauline; Matcham, Faith; White, Katie M; Oetzmann, Carolin; Simblett, Sara; ... Cummins, Nicholas; + view all (2023) Classifying depression symptom severity: Assessment of speech representations in personalized and generalized machine learning models. In: Procceedings of the International Speech Communication Association. (pp. pp. 1738-1742). ISCA: Dublin, Ireland. Green open access

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Abstract

There is an urgent need for new methods that improve the management and treatment of Major Depressive Disorder (MDD). Speech has long been regarded as a promising digital marker in this regard, with many works highlighting that speech changes associated with MDD can be captured through machine learning models. Typically, findings are based on cross-sectional data, with little work exploring the advantages of personalization in building more robust and reliable models. This work assesses the strengths of different combinations of speech representations and machine learning models, in personalized and generalized settings in a two-class depression severity classification paradigm. Key results on a longitudinal dataset highlight the benefits of personalization. Our strongest performing model set-up utilized self-supervised learning features and convolutional neural network (CNN) and long short-term memory (LSTM) back-end.

Type: Proceedings paper
Title: Classifying depression symptom severity: Assessment of speech representations in personalized and generalized machine learning models
Event: INTERSPEECH 2023
Open access status: An open access version is available from UCL Discovery
DOI: 10.21437/interspeech.2023-1721
Publisher version: https://doi.org/10.21437/interspeech.2023-1721
Language: English
Additional information: This version is the version of record. For information on re-use, please refer to the publisher’s terms and conditions.
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/10176456
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