Speech and Language Markers as Longitudinal Predictors of Youth Mental Health: A Systematic Review
Bibliographic record
Abstract
INTRODUCTION: Severe mental disorders in young people (< 25 years) are often preceded by subtle changes in communication and thinking, detectable in speech. Speech and language markers are promising for early detection; however, no systematic review has evaluated their prospective utility in predicting mental disorders in youth. We comprehensively reviewed longitudinal studies assessing speech/language markers as predictors of major mental disorder onset or symptom progression in youth. METHODS: We searched for longitudinal studies using recorded speech samples from youth or family members to predict diagnostic changes or symptom severity in major depressive disorder (MDD), psychosis, ADHD, substance use disorder, bipolar disorder, OCD and eating disorders. Risk of bias was assessed using the Newcastle-Ottawa Scale. Our protocol was pre-registered (CRD42024579798). RESULTS: Of 2260 articles, 11 studies met inclusion criteria, covering MDD (n = 3), psychosis (n = 5) and ADHD (n = 3). No eligible studies were found for OCD, substance use, bipolar or eating disorders. Both manual and computational speech analyses were used, with speech samples from parents and youth. Predictive speech/language markers included parental expressed emotion (MDD, ADHD), formal thought disorder (psychosis) and acoustic/linguistic features (psychosis, ADHD). Study quality was moderate to good (mean score: 5.45/8). CONCLUSIONS: Externally validated longitudinal studies on the predictive value of speech/language markers of youth-onset mental disorders are scarce, restricted to a few target disorders and do not allow for variations due to the developmental stage of the samples. Nonetheless, existing studies highlight the potential of applying Natural Language Processing methods to speech samples from both youth and parents for early identification.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 teacher head, 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".