Automated analysis of speech as a marker of sub-clinical psychotic experiences

Automated speech analysis techniques, when combined with artificial
intelligence and machine learning, show potential in capturing and predicting
a wide range of psychosis symptoms, garnering attention from researchers.
These techniques hold promise in predicting the transition to clinical psychosis
from at-risk states, as well as relapse or treatment response in individuals with
clinical-level psychosis. However, challenges in scientific validation hinder the
translation of these techniques into practical applications. Although sub-clinical
research could aid to tackle most of these challenges, there have been only few
studies conducted in speech and psychosis research in non-clinical populations.
This work aims to facilitate this work by summarizing automated speech
analytical concepts and the intersection of this field with psychosis research.
We review psychosis continuum and sub-clinical psychotic experiences, and
the benefits of researching them. Then, we discuss the connection between
speech and psychotic symptoms. Thirdly, we overview current and state
of-the art approaches to the automated analysis of speech both in terms of
language use (text-based analysis) and vocal features (audio-based analysis).
Then, we review techniques applied in subclinical population and findings in
these samples. Finally, we discuss research challenges in the field, recommend
future research endeavors and outline how research in subclinical populations
can tackle the listed challenges