Electroacoustic Comparison of Hearing Aid Output of Phonemes in Running Speech versus Isolation: Implications for Aided Cortical Auditory Evoked Potentials Testing
Bibliographic record
Abstract
Background. Functioning of nonlinear hearing aids varies with characteristics of input stimuli. In the past decade, aided speech evoked cortical auditory evoked potentials (CAEPs) have been proposed for validation of hearing aid fittings. However, unlike in running speech, phonemes presented as stimuli during CAEP testing are preceded by silent intervals of over one second. Hence, the present study aimed to compare if hearing aids process phonemes similarly in running speech and in CAEP testing contexts. Method. A sample of ten hearing aids was used. Overall phoneme level and phoneme onset level of eight phonemes in both contexts were compared at three input levels representing conversational speech levels. Results. Differences of over 3 dB between the two contexts were noted in one-fourth of the observations measuring overall phoneme levels and in one-third of the observations measuring phoneme onset level. In a majority of these differences, output levels of phonemes were higher in the running speech context. These differences varied across hearing aids. Conclusion. Lower output levels in the isolation context may have implications for calibration and estimation of audibility based on CAEPs. The variability across hearing aids observed could make it challenging to predict differences on an individual basis.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".