Methods of Diagnosing Speech Sound Disorders in Multilingual Children
Bibliographic record
Abstract
PURPOSE: Identification of speech sound disorder (SSD) in children who are multilingual is challenging for many speech-language pathologists (SLPs). This may be due to a lack of clinical resources to accurately identify SSD in multilingual children as easily as for monolingual children. The purpose of this article is to describe features of multilingual speech acquisition, identify evidence-based resources for the differential diagnosis of SSD in speakers of understudied language paradigms, and demonstrate how culturally responsive practices can be achieved in different linguistic contexts. METHOD: Examples of different approaches used to inform accurate diagnosis of SSD in 2- to 8-year-old multilingual children are described. The approaches used included (a) considering adult speech models, (b) completing validation studies, and (c) streamlining evidence-informed techniques. These methods were applied across four different language paradigms in countries within the Global North and Global South (e.g., Jamaican Creole-English, Jamaica; Vietnamese-English, Australia; French and additional languages, Belgium; Icelandic-Polish, Iceland). The culturally responsive nature of approaches in each cultural/linguistic setting is highlighted as well as the broader applicability of these approaches. RESULTS: Findings related to dialect-specific features, successful validation of tools to describe functional speech intelligibility and production accuracy, and the utility of different techniques applied in the diagnosis of SSD are outlined. CONCLUSIONS: Culturally responsive methods offer a useful framework for guiding SLPs' diagnostic practices. However, successful application of these practices is best operationalized at a local level in response to the linguistic, cultural, and geographic context. SUPPLEMENTAL MATERIAL: https://doi.org/10.23641/asha.29090000.
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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 | 0.020 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".