Perception of vowels with missing formant information
Bibliographic record
Abstract
The dominant formant-based model of vowel perception has been challenged by several whole-spectrum approaches. Recent arguments in favor of the importance of cues other than formant frequencies come from a study by Ito et al. [J. Acoust. Soc. Am., 110, 1141–1149] in which suppressing either of the first two formants did not radically change the identification of Japanese vowels. The present study replicates the experiment using the larger vowel system of the English language. Visual inspection shows that even when a formant is suppressed, listener responses do not deviate as much as would be expected if formants were the sole cue for vowel identification. However, quantitative analyses indicate that participant agreement in which vowel they heard is significantly lower when they respond to stimuli with suppressed formants. Additionally, the suppressed formant value becomes a less important predictor of vowel identity. These changes in responses become even larger when stimuli (original, F1-suppressed, and F2-suppressed vowels) are presented together rather than in separate blocks. Taken together, these results show that, although formants are not the only correlate to vowel identity, they seem to be the most important.
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".