Presenting and processing information in background noise: A combined speaker–listener perspective
Bibliographic record
Abstract
Transferring information orally in background noise is challenging, for both speaker and listener. Successful transfer depends on complex interaction between characteristics related to listener, speaker, task, background noise, and context. To fully assess the underlying real-life mechanisms, experimental design has to mimic this complex reality. In the current study, the effects of different types of background noise have been studied in an ecologically valid test design. Documentary-style information had to be presented by the speaker and simultaneously acquired by the listener in four conditions: quiet, unintelligible multitalker babble, fluctuating city street noise, and little varying highway noise. For both speaker and listener, the primary task was to focus on the content that had to be transferred. In addition, for the speakers, the occurrence of hesitation phenomena was assessed. The listener had to perform an additional secondary task to address listening effort. For the listener the condition with the most eventful background noise, i.e., fluctuating city street noise, appeared to be the most difficult with markedly longer duration of the secondary task. In the same fluctuating background noise, speech appeared to be less disfluent, suggesting a higher level of concentration from the speaker's side.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".