Speech recognition, loudness, and preference with extended bandwidth hearing aids for adult hearing aid users
Bibliographic record
Abstract
Objective: In contrast to the past, some current hearing aids can provide gain for frequencies above 4–5 kHz. This study assessed the effect of wider bandwidth on outcome measures using hearing aids fitted with the DSL v5.0 prescription.Design: There were two conditions: an extended bandwidth condition, for which the maximum available bandwidth was provided, and a restricted bandwidth condition, in which gain was reduced for frequencies above 4.5 kHz. Outcome measures were assessed in both conditions.Study sample: Twenty-four participants with mild-to-moderately-severe sensorineural high-frequency sloping hearing loss.Results: Providing extended bandwidth resulted in maximum audible output frequency values of 7.5 kHz on average for an input level of 65 dB SPL. An improvement in consonant discrimination scores (4.1%), attributable to better perception of /s/, /z/, and /t/ phonemes, was found in the extended bandwidth condition, but no significant change in loudness perception or preferred listening levels was found. Most listeners (79%) had either no preference (33%) or some preference for the extended bandwidth condition (46%).Conclusions: The results suggest that providing the maximum bandwidth available with modern hearing aids fitted with DSL v5.0, using targets from 0.25 to 8 kHz, can be beneficial for the tested population.
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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 | 0.004 |
| 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) | 0.003 | 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".