Sumby and Pollack revisted: The influence of live presentation on audiovisual speech perception.
Bibliographic record
Abstract
In their classic paper, Sumby and Pollack (1954) demonstrated that the sight of a talker’s face enhanced the identification of auditory speech in noise. Recently, there has been interest in the influence of some of their methodologies on audiovisual speech perception. Here, we examine the effects of presenting the audiovisual stimuli live, like Sumby and Pollack. Live presentation yields 3D visual stimuli, higher resolution images, and social conditions not present in modern replications with recorded stimuli and display monitors. Subjects were tested in pairs and alternated in the same session between a live talker (Live Condition) and a live feed of the talker to a television screen (Screen Condition). Order of presentation mode and word lists (monosyllabic English words) were counterbalanced across subjects. Subjects wore sound isolating headphones and signal intensity was controlled across conditions. Word lists were counterbalanced for spoken word frequency and initial consonant structure. Stimuli were presented in 7 signal-to-noise ratios (pink noise). Accuracy of identification was higher in the Live Condition than the Screen Condition. Possible causes of this effect are explored through manipulations of monocular and binocular depth cues and through testing with modern 3D display technology.
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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 | 0.005 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.004 | 0.007 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".