A Review of 30 Speech Assessments in 19 Languages Other Than English
Bibliographic record
Abstract
PURPOSE: In this study, the authors aimed to evaluate instruments designed to assess children's speech production in languages other than English. METHOD: Ninety-eight speech assessments in languages other than English were identified: 62 were commercially published, 17 published within journal articles, and 19 informal assessments. A review was undertaken of 30 commercially published assessments that could be obtained. RESULTS: The 30 instruments assessed 19 languages: Cantonese, Danish, Finnish, German, Greek, Japanese, Korean, Maltese-English, Norwegian, Pakistani-heritage languages (Mirpuri, Punjabi, Urdu), Portuguese, Putonghua (Mandarin), Romanian, Slovenian, Spanish, Swedish, and Turkish. The majority (70.0%) assessed speech sound production in monolingual speakers, 20.0% assessed one language of bilingual speakers, and 10.0% assessed both languages of bilingual speakers. All used single-word picture elicitation. Approximately half (53.3%) were norm-referenced, and the number of children in the normative samples ranged between 145 and 2,568. The remaining assessments were criterion-referenced (50.0%) (one fitted both categories). The assessments with English manuals met many of the psychometric criteria for operationalization; however, only 2 provided sensitivity and specificity data. CONCLUSIONS: Despite the varying countries of origin, there were many similarities between speech assessments in languages other than English. Few were designed for use with multilingual children, so validation is required for use in English-speaking contexts.
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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 | 0.014 | 0.066 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.023 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".