Lifting the curtain on the Wizard of Oz: Biased voice-based impressions of speaker size.
Bibliographic record
Abstract
The consistent, but often wrong, impressions people form of the size of unseen speakers are not random but rather point to a consistent misattribution bias, one that the advertising, broadcasting, and entertainment industries also routinely exploit. The authors report 3 experiments examining the perceptual basis of this bias. The results indicate that, under controlled experimental conditions, listeners can make relative size distinctions between male speakers using reliable cues carried in voice formant frequencies (resonant frequencies, or timbre) but that this ability can be perturbed by discordant voice fundamental frequency (F-sub-0, or pitch) differences between speakers. The authors introduce 3 accounts for the perceptual pull that voice F-sub-0 can exert on our routine (mis)attributions of speaker size and consider the role that voice F-sub-0 plays in additional voice-based attributions that may or may not be reliable but that have clear size connotations.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".