Effect of the Visual Presentation of a Craniofacial Syndrome on Speech Intelligibility in Noise
Bibliographic record
Abstract
Objective: Research has argued that a speaker’s facial appearance can result in an “intelligibility cost” for the listener. The study investigated whether such an intelligibility cost exists for a visible repaired cleft lip and nasal asymmetry. Setting: University department. Participants: Eight typical speakers provided speech samples. Twenty-eight naive listeners participated in a speech in noise experiment. Interventions: Listeners transcribed sentences in noise that were paired with faces of individuals with repaired cleft lip and nasal asymmetry or typical faces. They also rated speaker intelligibility and answered a questionnaire about their previous knowledge about cleft lip and palate. Main Outcome Measures: Percentage of words transcribed correctly and intelligibility ratings, compared by experimental condition (photo of typical face or face with repaired cleft lip and nasal asymmetry) and speaker gender. Results: There were no statistically significant differences between speech stimuli that were presented with faces with repaired cleft lip and nasal asymmetry or typical faces. The percentage of words transcribed correctly by the listeners was lower for female speakers ( F = 12.7, df = 1; P < .01). Speech intelligibility of female speakers was rated more poorly ( F = 10.5, df = 1; P < .01). Conclusions: Presence of a repaired cleft lip and nasal asymmetry did not result in an intelligibility cost for naive listeners. Future research should investigate possible effects of facial motion or previous knowledge.
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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 | 0.014 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".