The Influence of Semantic Context on the Intelligibility Benefit From Speech Glimpses in Younger and Older Adults
Bibliographic record
Abstract
PURPOSE: Speech is often masked by background sound that fluctuates over time. Fluctuations in masker intensity can reveal glimpses of speech that support speech intelligibility, but older adults have frequently been shown to benefit less from speech glimpses than younger adults when listening to sentences. Recent work, however, suggests that older adults may leverage speech glimpses as much, or more, when listening to naturalistic stories, potentially because of the availability of semantic context in stories. The current study directly investigated whether semantic context helps older adults benefit from speech glimpses released by a fluctuating (modulated) masker more than younger adults. METHOD: In two experiments, we reduced and extended semantic information of sentence stimuli in modulated and unmodulated speech maskers for younger and older adults. Speech intelligibility was assessed. RESULTS: We found that semantic context improves speech intelligibility in both younger and older adults. Both age groups also exhibit better speech intelligibility for a modulated than an unmodulated (stationary) masker, but the benefit from the speech glimpses was reduced in older compared to younger adults. Semantic context amplified the benefit gained from the speech glimpses, but there was no indication that the amplification by the semantic context led to a greater benefit in older adults. If anything, younger adults benefitted more. CONCLUSIONS: The current results suggest that the deficit in the masking-release benefit in older adults generalizes to situations in which extended speech context is available. That previous research found a greater benefit in older than younger adults during story listening may suggest that other factors, such as thematic knowledge, motivation, or cognition, may amplify the benefit from speech glimpses under naturalistic listening conditions.
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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 | 0.000 | 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".