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

Edward Campbell, Judith Dineley, Pauline Conde, Faith Matcham, Katie White, Carolin Oetzmann, Sara Simblett, Stuart Bruce, Amos Folarin, Til Wykes, Srinivasan Vairavan, Richard Dobson, Laura Docio-Fernandez, Carmen Garcia-Mateo, Vaibhav A Narayan, Matthew Hotopf, Nicholas Cummins, on behalf of the RADAR CNS Consortium

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

2 Citations (Scopus)

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.
Original languageEnglish
Title of host publicationProc. INTERSPEECH 2023
PublisherISCA-INST SPEECH COMMUNICATION ASSOC
Pages1738-1742
Number of pages5
Volume2023-August
DOIs
Publication statusPublished - 20 Aug 2023

Publication series

NameProceedings of the Annual Conference of the International Speech Communication Association, INTERSPEECH
ISSN (Print)2308-457X

Keywords

  • Major depressive disorder
  • personalization
  • self- supervised learning
  • remote monitoring technologies

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