Changes in conversational behavior across noise, language proficiency, and task
Bibliographic record
Abstract
Conversational turn-taking is a core feature of spoken interaction, yet prior research has typically examined the effects of background noise, second-language use, and task demands separately or in limited combinations, often under constrained or artificial conditions. While speakers can adapt to each challenge individually, little is known about how these factors jointly shape the flow and structure of natural dialogue. In this study, pairs of normal-hearing, native Ukrainian bilinguals engaged in both spontaneous, free-form conversations and a structured “spot the difference” task (Diapix UK) across quiet and noisy settings, in both their native (Ukrainian) and second (English) languages. This fully crossed, within-pair design enables a systematic comparison of how language, noise, and task interact to influence behavioral changes in speech production (e.g., articulation rate, vocal intensity) and conversational dynamics (e.g., floor transfer offsets, interpausal units, pause duration, and turn length). Findings from this work will advance our understanding of how multiple simultaneous demands shape spontaneous conversation, revealing how bilingual speakers manage timing, coordination, and clarity under both cognitive and environmental pressure.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".