International Collegium of Rehabilitative Audiology (ICRA) recommendations for the construction of multilingual speech tests
Bibliographic record
Abstract
OBJECTIVE: To provide guidelines for the development of two types of closed-set speech-perception tests that can be applied and interpreted in the same way across languages. The guidelines cover the digit triplet and the matrix sentence tests that are most commonly used to test speech recognition in noise. They were developed by a working group on Multilingual Speech Tests of the International Collegium of Rehabilitative Audiology (ICRA). DESIGN: The recommendations are based on reviews of existing evaluations of the digit triplet and matrix tests as well as on the research experience of members of the ICRA Working Group. They represent the results of a consensus process. RESULTS: The resulting recommendations deal with: Test design and word selection; Talker characteristics; Audio recording and stimulus preparation; Masking noise; Test administration; and Test validation. CONCLUSIONS: By following these guidelines for the development of any new test of this kind, clinicians and researchers working in any language will be able to perform tests whose results can be compared and combined in cross-language studies.
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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 | 0.126 | 0.254 |
| Meta-epidemiology (narrow) | 0.004 | 0.003 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.021 | 0.011 |
| Science and technology studies | 0.005 | 0.006 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.013 | 0.008 |
| Research integrity | 0.020 | 0.016 |
| Insufficient payload (model declined to judge) | 0.008 | 0.014 |
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".