Noisy voice or voice in noise: Evaluation of cognitive load on the speaker, work in progress
Bibliographic record
Abstract
We are comparing the cognitive load induced by various types of noise in the processing of information from speech. We examine if there is a difference in cognitive load between external noise sources (background noise) and internal noise sources (dysphonic voice). Our hypothesis is that noisy voices could be more cognitively demanding than background noise because they are more similar to the target signal spatially and temporally and the perceived link with the target signal is stronger. 60 normal hearing subjects (18-30 years) listen to texts in ten different conditions: (1) Healthy voice in multitalker babble noise, (2-4) Three types of dysphonic voices in background noise, (5-7) Three dysphonic voices in silence, (8) Healthy voice in silence, (9) Healthy voice with omnidirectional noise equivalent to dysphonic noise, (10) Healthy voice with dysphonic noise from same direction. We evaluate cognitive load in 4 ways: (1) during the listening phase by subjects’ performance on a secondary graphic task, (2) after the listening phase by subjects’ answers to a multi choice questionnaire and (3) by a free recall of the text, (4) by subjects’ grading of their listening effort. Methodological considerations will be discussed and preliminary results will be presented.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".