Conversational Dynamics in Task Dialogue Between Interlocutors With and Without Hearing Impairment
Bibliographic record
Abstract
This study investigated the effects of noise and hearing impairment on conversational dynamics between pairs of young normal-hearing and older hearing-impaired interlocutors. Twelve pairs of normal-hearing and hearing-impaired individuals completed a spot-the-difference task in quiet and in three levels of multitalker babble. To achieve the rapid response timing of turn taking that has been observed in normal conversations, people must simultaneously comprehend incoming speech, plan a response, and predict when their partners will end their turn. In difficult conditions, we hypothesized that the timing of turn taking by both normal-hearing and hearing-impaired interlocutors would be delayed and more variable. We found that the timing of turn starts by talkers with hearing impairment had higher variability than those with normal hearing, and participants with both normal hearing and hearing impairment started turns later and with more variability in the presence of noise. Overall, in the presence of noise, talkers spoke louder and slower, increased the duration of their pauses but decreased their rate of occurrence, and produced longer interpausal units, that is, units of connected speech surrounded by silence. However, when compared to previous studies of conversations between normal-hearing partners, the pattern of changes in conversational behavior by the normal-hearing participants was very different in the most challenging noise condition. The extent to which these adaptations are made to reduce the difficulty experienced by their partner with hearing impairment vs. the difficulty they experience themselves is not clear.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".