ArticleClinical psychological science : a journal of the Association for Psychological Science2022
High Predictive Accuracy of Negative Schizotypy With Acoustic Measures.
Article in Clinical psychological science : a journal of the Association for Psychological Science, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Humanizing "multimodal" digital phenotyping of serious mental illness.Journal of psychopathology and clinical science · 2026Article
- Vocal Markers of Schizophrenia: Assessing the Generalizability of Machine Learning Models and Their Clinical Applicability.Schizophrenia bulletin · 2026Article
- Fabla: A voice-based ecological assessment method for securely collecting spoken responses to researcher questions.Behavior research methods · 2025Article
- Detecting schizophrenia, bipolar disorder, psychosis vulnerability and major depressive disorder from 5 minutes of online-collected speech.Translational psychiatry · 2025Article
- Automated analysis of speech as a marker of sub-clinical psychotic experiences.Frontiers in psychiatry · 2023Review
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Authors and funding
7 authors.
Funding
Abstract
Negative schizotypal traits potentially can be digitally phenotyped using objective vocal analysis. Prior attempts have shown mixed success in this regard, potentially because acoustic analysis has relied on small, constrained feature sets. We employed machine learning to (a) optimize and cross-validate predictive models of self-reported negative schizotypy using a large acoustic feature set, (b) evaluate model performance as a function of sex and speaking task, (c) understand potential mechanisms underlying negative schizotypal traits by evaluating the key acoustic features within these models, and (d) examine model performance in its convergence with clinical symptoms and cognitive functioning. Accuracy was good (> 80%) and was improved by considering speaking task and sex. However, the features identified as most predictive of negative schizotypal traits were generally not considered critical to their conceptual definitions. Implications for validating and implementing digital phenotyping to understand and quantify negative schizotypy are discussed.
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