The Effect of Adaptive Nonlinear Frequency Compression on Phoneme Perception
Bibliographic record
Abstract
PURPOSE: This study implemented a fitting method, developed for use with frequency lowering hearing aids, across multiple testing sites, participants, and hearing aid conditions to evaluate speech perception with a novel type of frequency lowering. METHOD: A total of 8 participants, including children and young adults, participated in real-world hearing aid trials. A blinded crossover design, including posttrial withdrawal testing, was used to assess aided phoneme perception. The hearing aid conditions included adaptive nonlinear frequency compression (NFC), static NFC, and conventional processing. RESULTS: Enabling either adaptive NFC or static NFC improved group-level detection and recognition results for some high-frequency phonemes, when compared with conventional processing. Mean results for the distinction component of the Phoneme Perception Test (Schmitt, Winkler, Boretzki, & Holube, 2016) were similar to those obtained with conventional processing. CONCLUSIONS: Findings suggest that both types of NFC tested in this study provided a similar amount of speech perception benefit, when compared with group-level performance with conventional hearing aid technology. Individual-level results are presented with discussion around patterns of results that differ from the group average.
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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 | 0.006 |
| 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) | 0.002 | 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".