Auditory Processing Disorder and Speech Perception Problems in Noise: Finding the Underlying Origin
Bibliographic record
Abstract
PURPOSE: A hallmark listening problem of individuals presenting with auditory processing disorder (APD) is their poor recognition of speech in noise. The underlying perceptual problem of the listening difficulties in unfavorable listening conditions is unknown. The objective of this article was to demonstrate theoretically how to determine whether the speech recognition problems are related to an auditory dysfunction, a language-based dysfunction, or a combination of both. METHOD: Tests such as the Speech Perception in Noise (SPIN) test allow the exploration of the auditory and language-based functions involved in speech perception in noise, which is not possible with most other speech-in-noise tests. Psychometric functions illustrating results from hypothetical groups of individuals with APD on the SPIN test are presented. This approach makes it possible to postulate about the origin of the speech perception problems in noise. CONCLUSION: APD is a complex and heterogeneous disorder for which the underlying deficit is currently unclear. Because of their design, SPIN-like tests can potentially be used to identify the nature of the deficits underlying problems with speech perception in noise for this population. A better understanding of the difficulties with speech perception in noise experienced by many listeners with APD should lead to more efficient intervention programs.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".