Evaluation of wideband frequency responses and non-linear frequency compression for children with mild to moderate high-frequency hearing loss
Bibliographic record
Abstract
OBJECTIVE: To evaluate wideband amplification and non-linear frequency compression (NLFC) as a means to improve speech recognition for children with mild/moderate hearing loss. DESIGN: Randomized within-subject design with repeated measures across test conditions. STUDY SAMPLE: Eleven children with mild to moderate hearing loss were evaluated with: (1) Phonak BTE without NLFC, (2) Phonak BTE with NLFC, and (3) Oticon BTE with wideband response extending to 8000 Hz. RESULTS: Use of NLFC provided better detection and recognition of high-frequency stimuli (e.g. /sh/ and /s/). No difference in performance between conditions was observed for speech recognition when measured with the University of Western Ontario (UWO) plurals test and the UWO distinctive features difference test. Finally, there were no differences between conditions on the BKB-SIN test. CONCLUSIONS: Children with mild to moderate hearing loss have good access to high-frequency phonemes presented at fixed levels (e.g. 50 to 60 dBA) with both wideband and NLFC technology. Similarly, sentence recognition in noise was similar with wideband and NLFC. Adaptive test procedures that probe performance at lower input levels showed small but significant improvements in the detection and recognition of the phonemes /s/ and /sh/ with NLFC condition when compared to the NLFC Off and wideband conditions.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".