Evaluating the Effort Expended to Understand Speech in Noise Using a Dual-Task Paradigm: The Effects of Providing Visual Speech Cues
Bibliographic record
Abstract
PURPOSE: Using a dual-task paradigm, 2 experiments (Experiments 1 and 2) were conducted to assess differences in the amount of listening effort expended to understand speech in noise in audiovisual (AV) and audio-only (A-only) modalities. Experiment 1 had equivalent noise levels in both modalities, and Experiment 2 equated speech recognition performance levels by increasing the noise in the AV versus A-only modality. METHOD: Sixty adults were randomly assigned to Experiment 1 or Experiment 2. Participants performed speech and tactile recognition tasks separately (single task) and concurrently (dual task). The speech tasks were performed in both modalities. Accuracy and reaction time data were collected as well as ratings of perceived accuracy and effort. RESULTS: In Experiment 1, the AV modality speech recognition was rated as less effortful, and accuracy scores were higher than A only. In Experiment 2, reaction times were slower, tactile task performance was poorer, and listening effort increased, in the AV versus the A-only modality. CONCLUSIONS: At equivalent noise levels, speech recognition performance was enhanced and subjectively less effortful in the AV than A-only modality. At equivalent accuracy levels, the dual-task performance decrements (for both tasks) suggest that the noisier AV modality was more effortful than the A-only modality.
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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 | 0.005 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".