ArticleSchizophrenia bulletin2023
Semantic and Acoustic Markers in Schizophrenia-Spectrum Disorders: A Combinatory Machine Learning Approach.
Article in Schizophrenia bulletin, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
22 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Speech-based computational approaches for classification and symptom monitoring in schizophrenia spectrum disorders: a systematic review and meta-analysis.BMC psychiatry · 2026Pooled it
- Article
- Cross-linguistic benchmarking of NLP metrics for psychosis research.NPJ digital medicine · 2026Article
- Structural neuroimaging correlates of semantic incoherence in schizophrenia.European archives of psychiatry and clinical neuroscience · 2026Article
- An integrative NLP framework identifies multilevel linguistic phenotypes of schizophrenia across tasks.Psychological medicine · 2026Article
- From thought to language: Comparing schizophrenia spectrum disorders and Wernicke's aphasia with machine learning and LLMs.Schizophrenia research. Cognition · 2026Article
- Reduced linguistic coherence in psychosis defies semantic similarity accounts and relates to altered large-scale cortical hierarchy.Scientific reports · 2026Article
- Uncertainty modeling in multimodal speech analysis across the psychosis spectrum.NPJ digital medicine · 2026Article
- Turn-taking fluency in free conversations with individuals diagnosed with schizophrenia.Schizophrenia (Heidelberg, Germany) · 2025Article
- Hearing vocals to recognize schizophrenia: speech discriminant analysis with fusion of emotions and features based on deep learning.BMC psychiatry · 2025Article
- Speech analytics across the schizophrenia spectrum disorders: multimodal natural language processing and machine learning modelling in a Chinese-speaking population.Frontiers in psychiatry · 2025Article
- Measurement of schizophrenia symptoms through speech analysis from PANSS interview recordings.Frontiers in psychiatry · 2025Article
- Deeper insight into speech characteristics of patients at ultra-high risk using classification and explainability models.Frontiers in psychiatry · 2025Article
- Changes in the structure of spontaneous speech predict the disruption of hierarchical brain organization in first-episode psychosis.Human brain mapping · 2024Article
- Natural Language Processing and Schizophrenia: A Scoping Review of Uses and Challenges.Journal of personalized medicine · 2024Article
- Trajectories through semantic spaces in schizophrenia and the relationship to ripple bursts.Proceedings of the National Academy of Sciences of the United States of America · 2023Article
- Theory-Driven Analysis of Natural Language Processing Measures of Thought Disorder Using Generative Language Modeling.Biological psychiatry. Cognitive neuroscience and neuroimaging · 2023Article
- Language and Psychosis: Tightening the Association.Schizophrenia bulletin · 2023Article
- Syntactic Network Analysis in Schizophrenia-Spectrum Disorders.Schizophrenia bulletin · 2023Article
- Language Network Dysfunction and Formal Thought Disorder in Schizophrenia.Schizophrenia bulletin · 2023Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
background and hypothesisSpeech is a promising marker to aid diagnosis of schizophrenia-spectrum disorders, as it reflects symptoms like thought disorder and negative symptoms. Previous approaches made use of different domains of speech for diagnostic classification, including features like coherence (semantic) and form (acoustic). However, an examination of the added value of each domain when combined is lacking as of yet. Here, we investigate the acoustic and semantic domains separately and combined. STUDY
designUsing semi-structured interviews, speech of 94 subjects with schizophrenia-spectrum disorders (SSD) and 73 healthy controls (HC) was recorded. Acoustic features were extracted using a standardized feature-set, and transcribed interviews were used to calculate semantic word similarity using word2vec. Random forest classifiers were trained for each domain. A third classifier was used to combine features from both domains; 10-fold cross-validation was used for each model.
resultsThe acoustic random forest classifier achieved 81% accuracy classifying SSD and HC, while the semantic domain classifier reached an accuracy of 80%. Joining features from the two domains, the combined classifier reached 85% accuracy, significantly improving on separate domain classifiers. For the combined classifier, top features were fragmented speech from the acoustic domain and variance of similarity from the semantic domain.
conclusionsBoth semantic and acoustic analyses of speech achieved ~80% accuracy in classifying SSD from HC. We replicate earlier findings per domain, additionally showing that combining these features significantly improves classification performance. Feature importance and accuracy in combined classification indicate that the domains measure different, complementing aspects of speech.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.