The articulatory basis of phonological error patterns in childhood speech sound disorders
Bibliographic record
Abstract
Speech acquisition involves complex coordination of articulatory structures, primarily the jaw, lips, and tongue. Typically developing children acquire speech sounds in a hierarchical sequence governed by progressive neuromotor maturation. However, disruptions in speech motor control can lead to systematic phonological error patterns, commonly attributed to cognitive-linguistic deficits. This study explores the articulatory foundations of phonological error patterns in preschool-aged children diagnosed with moderate-to-severe speech sound disorders. Using data from 48 children who participated in a randomized controlled trial, we employed Mutual Information Analysis and Random Forest Models to quantify associations between specific speech motor limitations and phonological error patterns identified via the Diagnostic Evaluation of Articulation and Phonology assessment. Results showed moderate associations between phonological error patterns in particular cluster reduction, final consonant deletion, stopping, gliding, and atypical errors and limitations in jaw, labial-facial, and lingual control. Gliding, unexpectedly, demonstrated a negative relationship with speech motor errors, being more prevalent among older and milder cases, suggesting it may reflect a compensatory developmental stage rather than purely phonological simplification. These findings highlight the significance of speech motor constraints in phonological error patterns, challenging traditional cognitive-linguistic explanations. The results support theoretical frameworks such as Articulatory Phonology, emphasizing that phonological error patterns are influenced substantially by articulatory and neuromotor development. This study underscores the need for integrating motor considerations into clinical assessments and interventions for speech sound disorders in children, fostering a comprehensive approach bridging cognitive-linguistic and motor speech perspectives.
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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 | 0.003 | 0.012 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".