ArticleSchizophrenia bulletin2023
Latent Factors of Language Disturbance and Relationships to Quantitative Speech Features.
Article in Schizophrenia bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. An erratum has been issued. Cited by 19 papers, 1 of them a synthesis that pooled it.
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Who cites it
19 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Voice Patterns as Markers of Schizophrenia: Building a Cumulative Generalizable Approach Via a Cross-Linguistic and Meta-analysis Based Investigation.Schizophrenia bulletin · 2023Pooled it
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Reading between the lines: Combining pause dynamics and semantic coherence for automated assessment of thought disorder.Neuropsychologia · 2026Article
- Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability.Schizophrenia bulletin · 2026Article
- Linking speech patterns to brain structure in affective and psychotic disorders: an integrative natural language processing approach.Molecular psychiatry · 2026Article
- Disorganisation and depression: a re-examination of how we think and speak when depressed.European archives of psychiatry and clinical neuroscience · 2026Review
- Unique signatures in verbal fluency task performance in schizophrenia and depression.Schizophrenia research. Cognition · 2026Article
- Clinical psychopathology-based early relapse prediction model using speech and language in psychosis.Schizophrenia research. Cognition · 2026Review
- Diagnostic Utility of Formal Thought Disorder Between Affective and Non-Affective Psychosis: A Machine Learning Approach.The Psychiatric quarterly · 2026Article
- Linking spontaneous speech, cognition, and psychopathology across affective and psychotic disorders: A network approach.European psychiatry : the journal of the Association of European Psychiatrists · 2026Article
- Evaluating the utility of large language models for detecting and simulating language dysfunction.Frontiers in artificial intelligence · 2026Article
- Transdiagnostic types of formal thought disorder and their association with gray matter brain structure: a model-based cluster analytic approach.Molecular psychiatry · 2025Article
- Sentiment in speech is associated with symptom severity in psychosis.Cognitive neuropsychiatry · 2025Article
- Editorial: Natural language processing and artificial intelligence tools to explore the relationship between language and schizophrenia from diagnosis to care.Frontiers in psychiatry · 2025Article
- Automated speech and language markers of longitudinal changes in psychosis symptoms.NPP - digital psychiatry and neuroscience · 2025Article
- Brain structural associations of syntactic complexity and diversity across schizophrenia spectrum and major depressive disorders, and healthy controls.Schizophrenia (Heidelberg, Germany) · 2024Article
- Narrative Forewarnings: A Qualitative Analysis of the Themes Preceding Disorganized Speech in Schizophrenia.Behavioral sciences (Basel, Switzerland) · 2024Article
- Language and Psychosis: Tightening the Association.Schizophrenia bulletin · 2023Article
- Language Network Dysfunction and Formal Thought Disorder in Schizophrenia.Schizophrenia bulletin · 2023Review
Corrections and comments
- Erratum issued
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and hypothesisQuantitative 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
designClinical 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
resultsWe 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.
conclusionsWe 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.
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