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.