Computational speech markers of negative symptoms show evidence of being robust to antipsychotic dose and extrapyramidal symptoms in schizophrenia
Bibliographic record
Abstract
There is increasing interest in using computational speech and language analysis for the objective assessment of symptom severity in schizophrenia spectrum disorders (SSD), including negative symptom severity. However, the extent to which antipsychotic side effects influence speech markers of negative symptoms is unclear. This study investigated associations between computational speech and negative symptom severity, and examined whether speech features are associated with antipsychotic dose and extrapyramidal symptoms (EPS). Sixty-seven participants with SSD completed speech-focused tasks and clinical assessment. Seventeen speech features capturing relevant acoustic, timing, and linguistic characteristics were extracted from participant responses and examined for associations with negative symptoms, EPS, and antipsychotic dose. Eight timing features and one linguistic feature were significantly correlated with negative symptom severity (p < .05, false-discovery-rate-corrected; FDR), and these relationships were specific to negative symptoms rather than overall psychiatric symptom severity. No features were significantly correlated with antipsychotic dose or EPS after FDR correction, and Bayesian analyses provided moderate evidence for the null hypothesis (i.e., absence of an association) for most features. Two features showed Bayesian evidence for the alternative hypothesis, however; the association between speech and negative symptoms remained significant after removing the clinical covariate effects. The results provide support for computational speech markers of negative symptom severity, and both frequentist and Bayesian evidence that these speech markers are generally not confounded by antipsychotic side effects. The results help advance the clinical validation of computational speech assessment and analysis for measuring negative symptom severity in SSD.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".