Voices in noise or noisy voices: Effects on task performance and appreciation
Bibliographic record
Abstract
Learning from orally presented information often requires distinguishing target signals from noise. Effects of background noise on information processing has been extensively studied, and it is clear that similarity between target and noise makes separation more difficult. Therefore, the question is what happens when noise is actually part of the target signal, which is the case for dysphonic voices. Dysphonia is defined as a speech disorder `characterized by the abnormal production and/or absences of vocal quality, pitch, loudness, resonance, and/or duration, which is inappropriate for an individual's age and/or sex.' (ASHA). In this study, information processing is investigated in two noise conditions that are thought to be very challenging: multitalker babble and dysphonic voices. The aim is to compare the effect of a noise source that is very similar to the target signal (speech) but clearly external, with the effect of noise sources that are inherently part of the signal (dysphonia). In addition, the combined effect, i.e. a dysphonic voice in multitalker babble, is studied as well. For information processing, task performance and subjective perception of difficulty are evaluated. Subjective perception varies most clearly with the different noise conditions. Reported difficulty increases significantly for multitalker babble and dysphonia separately, both compared to a healthy voice in quiet conditions. Remarkably, within multitalker babble no differences in rating between dysphonic voices and the healthy voice are seen; dysphonic voices are no longer rated more difficult than a healthy voice when this healthy voice is also presented within babble noise.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".