Effects of Noise and Second Language on Conversational Dynamics in Task Dialogue
Bibliographic record
Abstract
This study provides a framework for measuring conversational dynamics between conversational partners (interlocutors). Conversations from 20 pairs of young, normal-hearing, native-Danish talkers were recorded when speaking in both quiet and noise (70 dBA sound pressure level [SPL]) and in Danish and English. Previous studies investigating the intervals from when one talker stops talking to when the next one starts, termed floor-transfer offsets (FTOs), suggest that typical turn-taking requires interlocutors to predict when the current talker will finish their turn. We hypothesized that adding noise and/or speaking in a second language (L2) would increase the communication difficulty and result in longer and more variable FTOs. The median and interquartile range of FTOs increased slightly in noise, and in L2, there was a small increase in interquartile range but a small decrease in the median of FTO durations. It took the participants longer to complete the task in both L2 and noise, indicating increased communication difficulty. The average duration of interpausal units, that is, units of connected speech surrounded by silences of 180 ms or more, increased by 18% in noise and 8% in L2. These findings suggest that talkers held their turn for longer, allowing more time for speech understanding and planning. In L2, participants spoke slower, and in both L2 and noise, they took fewer turns. These changes in behavior may have offset some of the increased difficulty when communicating in noise or L2. We speculate that talkers prioritize the maintenance of turn-taking timing over other speech measures.
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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 | 0.003 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".