The effect of a hearing aid noise reduction algorithm on the acquisition of novel speech contrasts
Bibliographic record
Abstract
Audiologists are reluctant to prescribe digital hearing aids with active digital noise reduction (DNR) to pre-verbal children due to their potential for an adverse effect on the acquisition of language. The present study investigated the relation between DNR and language acquisition by modeling pre-verbal language acquisition using adult listeners presented with a non-native speech contrast. Two groups of normal-hearing, monolingual Anglophone subjects were trained over four testing sessions to discriminate novel, difficult to discriminate, non-native Hindi speech contrasts in continuous noise, where one group listened to both speech items and noise processed with DNR, and where the other group listened to unprocessed speech in noise. Results did not reveal a significant difference in performance between groups across testing sessions. A significant learning effect was noted for both groups between the first and second testing sessions only. Overall, DNR does not appear to enhance or impair the acquisition of novel speech contrasts by adult listeners.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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) | 0.000 | 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 teacher head, 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".