Speech and the n-Back task as a lens into depression. How combining both may allow us to isolate different core symptoms of depression
Salvatore Fara, Stefano Goria, Emilia Molimpakis, Nicholas Cummins

TL;DR
This study explores how combining speech analysis with n-Back working memory assessments can better identify specific depression symptoms, revealing distinct associations with speech and cognitive markers.
Contribution
It introduces a large multi-modal dataset and demonstrates that integrating speech and n-Back features enhances depression prediction and symptom-specific understanding.
Findings
Speech and n-Back features improve depression classification accuracy.
Different depression symptoms are differentially associated with speech and cognitive markers.
Somatic symptoms correlate more with n-Back performance, while mood-related symptoms relate to speech changes.
Abstract
Embedded in any speech signal is a rich combination of cognitive, neuromuscular and physiological information. This richness makes speech a powerful signal in relation to a range of different health conditions, including major depressive disorders (MDD). One pivotal issue in speech-depression research is the assumption that depressive severity is the dominant measurable effect. However, given the heterogeneous clinical profile of MDD, it may actually be the case that speech alterations are more strongly associated with subsets of key depression symptoms. This paper presents strong evidence in support of this argument. First, we present a novel large, cross-sectional, multi-modal dataset collected at Thymia. We then present a set of machine learning experiments that demonstrate that combining speech with features from an n-Back working memory assessment improves classifier performance…
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Taxonomy
TopicsMental Health Research Topics · Emotion and Mood Recognition · Mental Health via Writing
