Physiological prediction of masking release for normal-hearing and hearing-impaired listeners
Bibliographic record
Abstract
Léger et al. [J. Acoust. Soc. Am. (2012)] measured the intelligibility of speech in steady and spectrally or temporally modulated maskers for stimuli filtered into low- (<1.5 kHz) and mid-frequency (1–3 kHz) regions. Listeners with high-frequency hearing loss but near to clinically normal audiograms in the low- and mid-frequency regions showed poorer performance than a control group with normal hearing, but showed preserved spectral and temporal masking release. Here, we investigated whether a physiologically accurate model of the auditory periphery [Zilany et al., J. Acoust. Soc. Am. (2009)] can explain these masking release data. Intelligibility was predicted using the Neurogram SIMilarity (NSIM) metric of Hines and Harte [Speech Commun. (2010) and (2012)]. This metric can make use of either an “all-information” neurogram with small time bins or a “mean-rate” neurogram with large time bins. The average audiograms of the different groups of listeners from the study of Léger et al. were simulated in the model by applying different mixes of outer and/or inner hair cell impairment. Very accurate predictions of the human data for both normal-hearing and hearing-impaired groups were obtained from the all-information NSIM metric (i.e., taking into account phase-locking information) with threshold shifts produced predominantly by OHC impairment (and minimal IHC impairment).
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".