Online speech assessment of the psychotic spectrum: Exploring the relationship between overlapping acoustic markers of schizotypy, depression and anxiety

Julianna Olah; Kelly Diederen; Toni Gibbs-Dean; Matthew J Kempton; Richard Dobson; Thomas Spencer; Nicholas Cummins

doi:10.1016/j.schres.2023.03.044

Online speech assessment of the psychotic spectrum: Exploring the relationship between overlapping acoustic markers of schizotypy, depression and anxiety

Schizophr Res. 2023 Sep:259:11-19. doi: 10.1016/j.schres.2023.03.044. Epub 2023 Apr 18.

Authors

Julianna Olah¹, Kelly Diederen², Toni Gibbs-Dean², Matthew J Kempton², Richard Dobson³, Thomas Spencer², Nicholas Cummins³

Affiliations

¹ Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK. Electronic address: julianna.olah@kcl.ac.uk.
² Institute of Psychiatry, Psychology and Neuroscience, Department of Psychosis Studies, King's College London, London SE5 8AF, UK.
³ Institute of Psychiatry, Psychology and Neuroscience, Department of Biostatistics & Health Informatics, King's College London, London SE5 8AF, UK.

PMID: 37080802
DOI: 10.1016/j.schres.2023.03.044

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 including generalized anxiety and depression symptoms in the model 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 with weak to moderate predictive power, and with moderate and good predictive power when basic demographic variables were added to the models. Most influential features of these models largely overlapped. The predictive power of speech marker-based models of schizotypy significantly improved after including symptom scores of depression and generalized anxiety in the models (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.

Keywords: Biomarkers; General population; Psychometric schizotypy; Speech analysis; Voice abnormalities.

MeSH terms

Anxiety / diagnosis
Cross-Sectional Studies
Depression / diagnosis
Humans
Schizotypal Personality Disorder* / complications
Schizotypal Personality Disorder* / diagnosis
Speech