Classifying depression symptom severity: assessment of speech representations in personalized and generalized machine learning models
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.
History
Publication status
- Published
File Version
- Accepted version
Publisher
ISCAPublisher URL
External DOI
Page range
1738-1742Event name
Interspeech 2023Event location
Dublin, IrelandEvent type
conferenceEvent date
August 20th to 24th 2023Department affiliated with
- Psychology Publications
Full text available
- Yes
Peer reviewed?
- Yes