Sources of variance in the audiovisual perception of speech in noise
Bibliographic record
Abstract
The sight of a talker’s face dramatically influences the perception of auditory speech. This effect is most commonly observed when subjects are presented audiovisual (AV) stimuli in the presence of acoustic noise. However, the magnitude of the gain in perception that vision adds varies considerably in published work. Here we report data from an ongoing study of individual differences in AV speech perception when English words are presented in an acoustically noisy background. A large set of monosyllablic nouns was presented at 7 signal-to-noise ratios (pink noise) in both AV and auditory-only (AO) presentation modes. The stimuli were divided into 14 blocks of 25 words and each block was equated for spoken frequency using the SUBTLEXus database (Brysbaert and New, 2009). The presentation of the stimulus blocks was counterbalanced across subjects for noise level and presentation. In agreement with Sumby and Pollack (1954), the accuracy of both AO and AV increase monotonically with signal strength with the greatest visual gain being when the auditory signal was weakest. These average results mask considerable variability due to subject (individual differences in auditory and visual perception), stimulus (lexical type, token articulation) and presentation (signal and noise attributes) factors. We will discuss how these sources of variance impede comparisons between studies.
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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 | 0.002 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".