Psychometric function for speech-in-noise tests accounts for word-recognition deficits in older listeners
Bibliographic record
Abstract
Speech-in-noise (SIN) understanding in older age is affected by hearing loss, impaired central auditory processing, and cognitive deficits. SIN-tests measure these factors' compound effects by a speech reception threshold, defined as the signal-to-noise ratio required for 50% word understanding (SNR50). This study compared two standard SIN tests, QuickSIN (n = 354) in young and older adults and BKB-SIN (n = 139) in older adults (>60 years). The effects of hearing loss and age on SIN understanding were analyzed to identify auditory and nonauditory contributions to SIN loss. Word recognition in noise was modelled with individual psychometric functions using a logistic fit with three parameters: the midpoint (SNRα), slope (β), and asymptotic word-recognition deficit at high SNR (λ). The parameters SNRα and λ formally separate SIN loss into two components. SNRα characterizes the steep slope of the psychometric function at which a slight SNR increase provides a considerable improvement in SIN understanding. SNRα was discussed as being predominantly affected by audibility and low-level central auditory processing. The parameter λ describes a shallow segment of the psychometric function at which a further increase in the SNR provides modest improvement in SIN understanding. Cognitive factors in aging may contribute to the SIN loss indicated by λ.
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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 | 0.006 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.002 | 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".