Leveraging Machine Learning for Acoustic Feature Analysis in Neurodevelopmental Disorders: Insights into Emotional Profiles
Bibliographic record
Abstract
Neurodevelopmental disorders (NDDs) in preschoolers often involve social and communication deficits, contributing to heightened anger, sadness, and anxiety. This study examined whether acoustic features of speech could detect emotional dysregulation in 65 French-speaking children (age 4) diagnosed with ADHD, developmental language disorder, psychosocial issues, or cognitive impairments. Using standardized assessments, participants were grouped by emotional/psychological, speech/language, or cognitive/motor difficulties. Audio recordings from structured and unstructured tasks were processed via openSMILE, generating 153 features capturing spectral, prosodic, and energy parameters linked to emotion. A random forest classifier compared these acoustic profiles to EmoDB samples labeled with negative emotions. Results showed that children with NDDs exhibited unique acoustic markers of negative emotions, though differences among subgroups were minimal. Attempts to pinpoint anxiety as a diagnostic feature were inconclusive. Overall, machine learning–based acoustic analysis holds promise for identifying emotional dysregulation, encouraging further multimodal approaches in clinical assessments, and more robust early interventions overall.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.017 | 0.024 |
| Science and technology studies | 0.003 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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; both teacher heads agree on what is shown here.
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".