Development of the Hearing In Noise Test (HINT) in Spanish
Bibliographic record
Abstract
Assessment of functional hearing ability is relevant to clinical outcome assessments, occupational health evaluations, and forensic applications. Communication with spoken language is a primary aspect of functional hearing ability. Comparable speech tests in multiple languages are required to make these assessments in a multilingual population. This presentation will report on an ongoing international research project to develop a Latin American Spanish version of the Hearing In Noise Test (HINT) for this purpose. The methods of selecting speech materials, recording the materials, synthesizing appropriate masking noise, equating the difficulty of the materials, forming sentence lists, norming the lists, and determining reliability and sensitivity will be discussed. Samples of Spanish, as well as other languages (English, Japanese, Mandarin, Cantonese, and Canadian French) will be used to demonstrate the procedures by which standard dialects of each language were selected. Cross-language comparisons of the spectral and temporal characteristics of the speech materials will be presented. Norms, reliability coefficients, and measurement errors for each language will also be reported. Methods for comparing and/or equating functional hearing ability across languages will be described. Finally, procedures for measuring functional hearing ability will be discussed.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".