Modelling the effects of cochlear impairment on the neural representation of speech in the auditory nerve and primary auditory cortex
Bibliographic record
Abstract
Accurate models of normal and impaired neural representations of sound are useful tools in understanding how acoustic stimuli are encoded in the brain, predicting speech intelligibility, and developing and testing speech processing schemes for hearing aids. In this paper we review recent developments in modelling the effects of hair cell impairment on neural responses to speech stimuli in the auditory nerve and primary auditory cortex. Several important cochlear nonlinearities, such as compression and suppression, the shift in tuning with sound pressure level, and the component-1/component-2 transition at very high sound pressure levels, have been incorporated into the latest models of the auditory periphery. These properties of cochlear processing prove to be important not only in forming the normal neural representation of sound but also in determining the degradation of the neural representation in cases of hair cell impairment. We have evaluated these models by using them to predict the effects of presentation level, hearing loss and amplification on speech intelligibility. The models are able to predict both the effects of audibility on speech intelligibility and the “roll over” in speech intelligibility at high presentation levels for normal hearing listeners and for hearing impaired listeners using hearing aids.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".