An Audiovisual-FM System (AudiSee) Designed for Use in Classroom Settings: An Evaluation of the Effects of Visual Distractions on Speechreading Performance
Bibliographic record
Abstract
In this article we describe the AudiSee system, an audiovisual-FM system designed primarily for use in classroom settings. First, the rationale underlying the development of AudiSee is discussed. Then, the components of the audiovisual-FM system are described. Finally, an experiment designed to evaluate the effects of visual background distractions on speechreading performance using AudiSee is presented. Three aspects of visual background distractions were investigated: (1) the orientation of AudiSee's mini-camera and a video monitor (portrait vs. landscape), (2) the nature of the visual backdrop (neutral/non-obtrusive vs. colorful/distracting), and (3) the magnitude of head movements made by the speaker while producing the test stimuli (none, moderate, and large amounts of head movement). Twenty sentences were recorded under each of the 12 experimental conditions (2 orientations × 2 visual backdrops × 3 head movement conditions). Fourteen subjects completed a closed-set sentence recognition task in a visual-alone modality (speechreading). The results failed to reveal any differences among the 12 experimental conditions investigated. These findings suggest that the visual background distractions recorded by AudiSee's mini-camera do not interfere with speechreading performance. KEYWORD Speechreading - audiovisual-FM system - audiovisual speech perception - visual speech perception - visual speech cues - visual distractions
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".