Advantage of talker differences and spatial separation for speech-on-speech listening in younger and older adults with good audiograms
Bibliographic record
Abstract
Older adults have more difficulty than younger adults understanding speech when there is competing speech, even if they have good audiograms. Age-related differences in listening may be due to declines in auditory temporal processing and/or cognition. We administered the LiSN-S (Cameron et al., 2011) to measure speech reception thresholds (SRTs) in younger and older adults with good audiograms. There were four test conditions, in which the target and competing speech were presented with the same or different voices at the same or different locations. Compared to younger listeners, older listeners obtained worse SRTs in all test conditions and they realized less advantage from talker differences and spatial separation between the target and competing speech. For both groups, the results obtained in the four test conditions were strongly associated with each other. We also assessed cognitive abilities and auditory temporal processing in the older adults. LiSN-S results in this group were strongly associated with measures of cognition, as well as pure-tone averages (PTA) for 9 and 10 kHz, but not PTAs for frequencies in the standard audiometric range or measures of temporal processing (tapping the use of fine structure and gap cues).
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".