Evaluation of Electroacoustic Test Signals II: Development and Cross-Validation of Correction Factors
Bibliographic record
Abstract
In Brief Objective To develop and cross-validate corrections for improving the match between amplified speech levels and frequency response measurements with hearing aids. Design Previously published correction approaches were reviewed. Two regression-based corrections and two nonregression corrections were developed from an existing database of hearing aid responses measured with clinically available test signals and speech (Scollie & Seewald, 2002). Corrections were evaluated on a second database of digital hearing aid responses for test signals and speech. The second data set was constructed specifically to challenge three hypothesized threats to the robustness of the corrections. Results The error for each signal (corrected and uncorrected) was calculated. Correction procedures produced a significant improvement in the match between predicted and measured aided levels of speech. Inclusion of compression-related variables provided small but significant improvements. Results generalized to the second data set. Conclusions Correction procedures may be applied to improve the match between aided test signal levels and aided levels of speech. For hearing aids with nonlinear processing, frequency response verification at different sound intensity levels is necessary. Although speech-weighted test signals tend to provide a more accurate estimate of aided speech levels than pure tones, they still do not provide an exact match to the levels of amplified speech. This mismatch is affected by circuit type and test level. The present study aimed to develop and cross-validate corrections for reducing the mismatch. Two regression-based corrections and two nonregression corrections were developed from an existing database of hearing aid responses measured with clinically available test signals and speech, and then the regressions were evaluated on a second database. Correction procedures produced a significant improvement in the match between predicted and measured aided levels of speech. Inclusion of compression-related variables provided small but significant improvements.
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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 | 0.031 | 0.132 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".