Impact of the Visual Clutter of a Room on Francophone Speakers' Speech Production and Perception (Extended Abstract)
Bibliographic record
Abstract
Speech perception and production are multimodal processes that are influenced by both auditory and visual cues.While the impact of acoustic environment has been heavily researched, few studies have investigated how environmental design influences the vocal behavior of speakers through visual perception.Our study explored the impact of visual clutter on the vocal production of speakers in terms of acoustic parameters such as SPL, f0, and CPP, and in terms of self-perception of speech.Participants had to perform two speech tasks, reciting the alphabet, and reading a children's story, in two rooms that were acoustically similar (RT = 0,7 and 0,6 sec; mean dBA = 30,66 and 30,53 respectively) but visually different.Room A was clutter-free whereas Room B was cluttered with everyday items.Analyses revealed that the acoustic vocal measures did not vary significantly between the rooms, however, participants reported a significantly lower vocal effort (p<0,001) in Room A. Qualitative analyses revealed that participants preferred speaking in Room A, and correlations revealed that their self-perceived vocal intensity (decreased in Room A, increased in Room B) did not match the objective results.Our results highlight the importance of collecting subjective perception of speakers in studies investigating vocal effort.
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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 | 0.000 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".