Formant-frequency trajectories as acoustic correlates to speech perception.
Bibliographic record
Abstract
Formant trajectories are excellent vowel discriminants; within vowel, they are nearly constant across speaker size, age, and sex, and across consonantal contexts. However, this model assumes that formant peaks are perceptually important and that human listeners track formant-frequency changes across time. Speech-recognition applications have avoided formant frequencies due to the difficulty of reliable formant tracking. In addition, it is not actually known whether human listeners do indeed follow formants perceptually across time. This paper presents results from several studies that examine the relationship between changing formant frequencies and perception. Alternative perceptual representations of vowels, such as global spectral shape, are precluded by evidence that individual formant amplitudes are largely ignored in vowel perception. In addition, where other spectral properties appear to have a perceptual effect, it is because stimuli have used formants that do not change. When formants are changing, perceptual effects of spectral shape properties disappear. In terms of human formant tracking, perceptual extrapolation of a formant sweep is mostly dependent on peak frequency and not other properties related to spectral shape. This demonstrates that listeners do indeed follow formant-frequency changes as auditory objects. Further research on formant frequency perception will be described.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".