Evaluation of Electroacoustic Test Signals I: Comparison with Amplified Speech
Bibliographic record
Abstract
In Brief Objective To evaluate the ability of clinical test signals to match the aided levels of real speech, across a range of hearing aid circuit types and strengths. Design Hearing aids (N = 41) were set to DSL targets for moderate, severe, and profound hearing losses. These hearing aids were tested with three test signals (Fonix Pure Tones, Fonix Composite Noise, and Audioscan Swept), as well as with running speech. The difference between the aided test signal and the aided speech was calculated. Results Accuracy of matches between aided test signals and aided speech levels depended on circuit type, signal type, and test level. Conclusions Clinical test signals can more accurately match the aided levels of speech for all types of hearing aids if they are 1) speech-weighted and 2) temporally modulated. Matches were more accurate at low to moderate test levels (i.e., 50 to 70 dB SPL), and less accurate at high test levels (i.e., 85 dB SPL). Selection of amplification includes matching appropriate electroacoustic characteristics of the hearing aid to the auditory characteristics and needs of the patient. This is done with a prescriptive formula, which provides a target frequency/gain function intended to amplify speech to given target levels across frequencies. A variety of clinical test signals then can be used to verify that the hearing aid meets the intended amplification targets. This study evaluated the ability of clinical test signals (some of which are more speech-like than others) to match the aided levels of real speech across a range of hearing aid circuit types and strengths. Accuracy of matches between aided test signals and aided speech levels depended on circuit type, signal type, and test level.
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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 | 0.001 |
| 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) | 0.000 | 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".