Using systematic synthetic voice differences to probe the perception of mixed-talker vowels.
Bibliographic record
Abstract
Many experiments have reported a perceptual advantage for vowels presented in blocked versus mixed speaker conditions. Nusbaum and colleagues [Nusbaum and Morin (1992); Magnuson and Nusbaum (2007)] found that the size of this advantage varies with aspects of the similarity of the voices involved. The largest performance difference occurs when voices are of intermediate similarity (e.g, two similar sounding female voices with incongruent vowel spaces). Difference are smaller when voices are either very similar or very different. The current study will explore some potentially relevant dimensions of similarity using vowels from a set of synthetic virtual talkers whose voice characteristics differ in formant ranges, f0, and/or source characteristics. Perceptual accuracy and response times will be collected using a speeded monitoring task in which participants are asked to respond as soon as they hear a specific target vowel. Results will be evaluated in light of current theories of vowel normalization and speaker adaptation.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".