Latent Factors of Language Disturbance and Relationships to Quantitative Speech Features. (22nd March 2023)

• Record Type: Journal Article
• Authors: Tang, Sunny X; Hänsel, Katrin; Cong, Yan; Nikzad, Amir H; Mehta, Aarush; Cho, Sunghye; Berretta, Sarah; Behbehani, Leily; Pradhan, Sameer; John, Majnu; Liberman, Mark Y
• Is Part Of: Schizophrenia bulletin. Volume 49(2023)Supplement 2
• Page Start: S93
• Page End: S103
• Publication Date: 2023-03-22
• Subjects: natural language processing -- schizophrenia -- psychosis -- thought disorder -- disorganization -- alogia -- graph analysis; Schizophrenia -- Periodicals; Schizophrenia -- Research -- Periodicals; 616.898005
• DOI: 10.1093/schbul/sbac145
• Languages: English
• ISSNs: 0586-7614

Abstract

Abstract: 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 = 334), including SSD ( n = 90). 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. The relationships between factor scores and computational speech features were examined for 202 of the participants. Study Results: We found a 3-factor model with a good fit in the cross-diagnostic group and an acceptable fit for the SSD subsample. The model identifies an impaired expressivity factor and 2 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. Each of the 3 factors had significant and distinct relationships with speech features, which differed for the cross-diagnostic vs 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.