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Embedded in any speech signal is a rich combination of cognitive,neuromuscular and physiological information. This richness makes speech apowerful signal in relation to a range of different health conditions,including major depressive disorders (MDD). One pivotal issue inspeech-depression research is the assumption that depressive severity is thedominant measurable effect. However, given the heterogeneous clinical profileof MDD, it may actually be the case that speech alterations are more stronglyassociated with subsets of key depression symptoms. This paper presents strongevidence in support of this argument. First, we present a novel large,cross-sectional, multi-modal dataset collected at Thymia. We then present a setof machine learning experiments that demonstrate that combining speech withfeatures from an n-Back working memory assessment improves classifierperformance when predicting the popular eight-item Patient Health Questionnairedepression scale (PHQ-8). Finally, we present a set of experiments thathighlight the association between different speech and n-Back markers at thePHQ-8 item level. Specifically, we observe that somatic and psychomotorsymptoms are more strongly associated with n-Back performance scores, whilstthe other items: anhedonia, depressed mood, change in appetite, feelings ofworthlessness and trouble concentrating are more strongly associated withspeech changes.

Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression