Physiological modeling for hearing aid design
Bibliographic record
Abstract
Physiological data from hearing-impaired cats suggest that conventional hearing aid signal-processing schemes do not restore normal auditory-nerve responses to a vowel [Miller et al., J. Acoust. Soc. Am. 101, 3602 (1997)] and can even produce anomalous and potentially confounding patterns of activity [Schilling et al., Hear. Res. 117, 57 (1998)]. These deficits in the neural representation may account at least partially for poor speech perception in some hearing aid users. An amplification scheme has been developed that produces neural responses to a vowel more like those seen in normal cats and that reduces confounding responses [Miller et al., J. Acoust. Soc. Am. 106, 2693 (1999)]. A physiologically accurate model of the normal and impaired auditory periphery would provide simpler and quicker testing of such potential hearing aid designs. Details of such a model, based on that of Zhang et al. [J. Acoust. Soc. Am. 109, 648 (2001)], will be presented. Model predictions suggest that impairment of both outer- and inner-hair cells contribute to the degraded representation of vowels in hearing-impaired cats. The model is currently being used to develop and test a generalization of the Miller et al. speech-processing algorithm described above to running speech. [Work supported by NIDCD Grants DC00109 and DC00023.] a)Now with the Dept. of Electrical and Computer Engineering, McMaster Univ., 1280 Main St. W., Hamilton, ON L8S 4K1, Canada.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.003 |
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".