The contribution of auditory temporal processing to the separation of competing speech signals in listeners with normal hearing
Bibliographic record
Abstract
The hallmark of auditory function in aging adults is difficulty listening in a background of competing talkers, even when hearing sensitivity in quiet is good. Age-related physiological changes may contribute by introducing small timing errors (jitter) to the neural representation of sound, compromising the fidelity of the signal’s fine temporal structure. This may preclude the association of spectral features to form an accurate percept of one complex stimulus, distinct from competing sounds. For simple voiced speech (vowels), the separation of two competing stimuli can be achieved on the basis of their respective harmonic (temporal) structures. Fundamental frequency (F0) differences in competing stimuli facilitate their segregation. This benefit was hypothesized to rely on the adequate temporal representation of the speech signal(s). Auditory aging was simulated via the desynchronization (∼0.25-ms jitter) of the spectral bands of synthesized vowels. The perceptual benefit of F0 difference for the identification of concurrent vowel pairs was examined for intact and jittered vowels in young adults with normal hearing thresholds. Results suggest a role for reduced signal fidelity in the perceptual difficulties encountered in noisy everyday environments by aging listeners. [Work generously supported by the Michael Smith Foundation for Health Research.]
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".