Auditory and cognitive aging: Differences between speech intelligibility and speech-in-speech intelligibility
Bibliographic record
Abstract
Older adults, even those with normal or near-normal audiograms, report greater subjective listening difficulty in everyday life compared to younger adults. However, their difficulties are not well predicted from pure-tone or speech audiometry in quiet or noise. Speech intelligibility tests based on word recognition do not incorporate many of the sensory and cognitive challenges that listeners confront in everyday situations where it is necessary to understand a target talker when there are competing talkers in the auditory scene. Speech-in-speech intelligibility is affected by age-related differences in auditory and cognitive processing. Age-related differences in auditory temporal processing reduce access to periodicity and temporal envelope cues that serve stream segregation and spatial listening. Age-related differences in cognitive processing are reflected in difficulty remembering speech heard in multi-talker babble or switching spatial attention when there is uncertainty about the location of a target talker in a multi-talker display. Furthermore, when listening occurs in multi-tasking conditions (e.g., listening while walking), there are increased and competing demands for cognitive resources that may affect performance on listening and/or the competing tasks. These examples highlight the importance of both inter- and intra-individual differences in speech-in-speech intelligibility that depend on the interaction of auditory and cognitive processing abilities.
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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 | 0.001 | 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) | 0.002 | 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".