Training listeners to report fundamental frequency and formant range information independently.
Bibliographic record
Abstract
The vowels of speakers of different sizes vary in terms of their average f0 and formant frequencies. In general, larger speakers produce vowels with lower formant frequencies and lower f0s. Listeners have demonstrated the ability to estimate the approximate size of a speaker and previous experiments have shown that these judgments are based on the joint consideration of f0 and formant range information. Thus both lower f0s and lower formant frequency ranges are associated with larger speakers by listeners. Studies which have asked listeners to evaluate voices have focused on the extraction of apparent speaker characteristics (which are informed by f0 and formant frequencies) rather than asking speakers to report f0 and formant range information directly. The current study consists of a training procedure by which participants will learn to report the f0 and formant range of voices independently. The training consists of a voice matching game in which participants hear a pair of vowels produced by a voice and are asked to indicate which of the candidate voices they just heard. Results will be analyzed in light of current theories of vowel perception and normalization.
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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 | 0.003 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".