Perceived Sound Quality Dimensions Influencing Frequency-Gain Shaping Preferences for Hearing Aid-Amplified Speech and Music
Bibliographic record
Abstract
Hearing aids are typically fitted using speech-based prescriptive formulae to make speech more intelligible. Individual preferences may vary from these prescriptions and may also vary with signal type. It is important to consider what motivates listener preferences and how those preferences can inform hearing aid processing so that assistive listening devices can best be tailored for hearing aid users. Therefore, this study explored preferred frequency-gain shaping relative to prescribed gain for speech and music samples. Preferred gain was determined for 22 listeners with mild sloping to moderately severe hearing loss relative to individually prescribed amplification while listening to samples of male speech, female speech, pop music, and classical music across low-, mid-, and high-frequency bands. Samples were amplified using a fast-acting compression hearing aid simulator. Preferences were determined using an adaptive paired comparison procedure. Listeners then rated speech and music samples processed using prescribed and preferred shaping across different sound quality descriptors. On average, low-frequency gain was significantly increased relative to the prescription for all stimuli and most substantially for pop and classical music. High-frequency gain was decreased significantly for pop music and male speech. Gain adjustments, particularly in the mid- and high-frequency bands, varied considerably between listeners. Music preferences were driven by changes in perceived fullness and sharpness, whereas speech preferences were driven by changes in perceived intelligibility and loudness. The results generally support the use of prescribed amplification to optimize speech intelligibility and alternative amplification for music listening for most 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".