Evaluation of an Audiovisual-FM System: Investigating the Interaction Between Illumination Level and a Talker’s Skin Color on Speech-Reading Performance
Bibliographic record
Abstract
A program designed to evaluate the benefits of an audiovisual-frequency modulated (FM) system led to some questions concerning the effects of illumination level and a talker's skin color on speech-reading performance. To address those issues, the speech of a Caucasian female was videotaped under 2 conditions: a light skin color condition and a dark skin color condition. For the latter condition, makeup was applied to the talker's face. For both skin color conditions, the talker was recorded while speaking sentences under 7 different levels of illumination: 2, 3, 4, 16, 60, 256, and 600 footcandles (fc). Fifteen participants completed the speech perception task in a visual-only modality. The results revealed a significant interaction of illumination level and skin color. For the light skin color condition, speech-reading performance improved systematically as the illumination level increased from 3 to 16 fc. For the dark skin color condition, no differences in speech-reading performance were observed between the 2-fc and the 3-fc conditions. However, a large improvement in speech-reading performance was observed as the illumination level increased from 4 fc to 16 fc. It is speculated that in addition to an overall effect of illumination level, the contrast in luminance at the level of the talker's face has an effect on speech-reading performance.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".