Online Speech Assessment of the Psychotic Spectrum: Exploring the relationship between overlapping acoustic markers of Schizotypy, Depression and Anxiety.

Abstract Background Remote assessment of acoustic alterations in speech holds promise to increase scalability and validity in research across the psychosis spectrum. A feasible first step in establishing a procedure for online assessments is to assess acoustic alterations in psychometric schizotypy. However, to date, the complex relationship between alterations in speech related to schizotypy and those related to comorbid conditions such as symptoms of depression and anxiety has not been investigated. This study tested whether (1) depression, generalized anxiety and high psychometric schizotypy have similar voice characteristics, (2) which acoustic markers of online collected speech are the strongest predictors of psychometric schizotypy, (3) whether controlling for generalized anxiety and depression symptoms can improve the prediction of schizotypy. Methods We collected cross-sectional, online-recorded speech data from 441 participants, assessing demographics, symptoms of depression, generalized anxiety and psychometric schizotypy. Results Speech samples collected online could predict psychometric schizotypy, depression, and anxiety symptoms, however, most influential features of these models largely overlapped. The predictive power of speech marker-based models of schizotypy significantly improved after controlling for symptoms of depression and generalized anxiety (from R2 = 0.296 to R2 = 0. 436). Conclusions Acoustic features of online collected speech are predictive of psychometric schizotypy as well as generalized anxiety and depression symptoms. The acoustic characteristics of schizotypy, depression and anxiety symptoms significantly overlap. Speech models that are designed to predict schizotypy or symptoms of the schizophrenia spectrum might therefore benefit from controlling for symptoms of depression and anxiety.