Information conveyed by <i>f</i>0 for vowel identification
Bibliographic record
Abstract
Our recent experiments with vocoded natural speech, wherein the spectral envelope and fundamental frequency are manipulated independently, have confirmed that some coordination of f0 and formant patterns are beneficial to vowel identification by humans. In an effort to model the perceptual dependency more precisely, we have investigated the performance of several alternative pattern recognition models on natural speech samples. This paper reports on several quite distinct methods of exploiting statistical relations between formant frequencies and f0 for recognition. Many of these methods yield quite similar results on the classic Peterson and Barney data and on larger, more recently collected data sets. Methods involving indirect normalization whereby the f0 of a single token is restricted to the role of estimating the formant frequency average of a speaker’s entire vowel system perform well. Indeed, they are often better than a method where the role of f0 is unconstrained, thus accommodating inherent pitch differences among vowels. The indirect use of f0 also allows for methods of combining f0 and formant range information in ways that preliminary results suggest to be more effective for modeling perceptual effects with modified stimuli. More formal evaluation against perceptual data will be presented.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".