Effects of background noise, talker’s voice, and speechreading on speech understanding by primary school children in simulated classroom listening situations
Bibliographic record
Abstract
In the primary school classroom, children are exposed to multiple factors that combine to create adverse conditions for listening to and understanding what the teacher is saying. Four experiments were conducted to investigate the combined effects of background babble noise (quiet vs. noise), voice quality (normal vs. dysphonic), and speechreading (audio-only vs. audio-visual cues) on speech understanding in 245 eight-year old children. Comprehension was tested using narratives from the test of Clinical Evaluation of Language Fundamentals. Background babble noise was composed of several children talking. Visual speech cues were presented using a digitally animated talker. Vocal loading was used to induce a dysphonic (hoarse) voice. Speech understanding was reduced by even low levels of babble noise, but compensated by visual cues. Dysphonia did not significantly reduce comprehension scores, but it was considered unpleasant. There was some evidence that performance in adverse conditions was positively associated with individual differences in cognitive executive function. Overall, these results suggest that multiple factors combine to influence speech understanding and listening effort for child listeners in the primary school classroom. The constellation of these room, talker, modality, and listener factors should be taken into account in the planning and design of educational and learning activities.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".