LAUSR

Background and Hypothesis Quantitative acoustic and textual measures derived from speech ("speech features") may provide valuable biomarkers for psychiatric disorders, particularly schizophrenia spectrum disorders (SSD). We sought to identify cross-diagnostic latent factors for speech disturbance with relevance for SSD and computational modeling. Study Design Clinical ratings for speech disturbance were generated across 14 items for a cross-diagnostic sample (N=343), including SSD (n=97). Speech features were quantified using an automated pipeline for brief recorded samples of free-speech. Factor models for the clinical ratings were generated using exploratory factor analysis, then tested with confirmatory factor analysis in the cross-diagnostic and SSD groups. Relationships among factor scores, speech features and other clinical characteristics were examined using network analysis. Study Results We found a 3-factor model with good fit in the cross-diagnostic group and acceptable fit for the SSD subsample. The model identifies an impaired expressivity factor and two interrelated disorganized factors for inefficient and incoherent speech. Incoherent speech was specific to psychosis groups, while inefficient speech and impaired expressivity showed intermediate effects in people with nonpsychotic disorders. Network analysis showed that the factors had distinct relationships with speech features, and that the patterns were different in the cross-diagnostic versus SSD groups. Conclusions We report a cross-diagnostic 3-factor model for speech disturbance which is supported by good statistical measures, intuitive, applicable to SSD, and relatable to linguistic theories. It provides a valuable framework for understanding speech disturbance and appropriate targets for modeling with quantitative speech features.