Modeling the effects of frequency shifts on vowel identification
Bibliographic record
Abstract
Previous experiments examining the effects of frequency shifts on vowel perception show that identification accuracy drops when the spectrum envelope is shifted up by more than about 150%, or shifted down by factors smaller than about 60% relative to adult male ranges. Such shifts produce formant patterns near the extreme limits found in human voices. But these effects interact with fundamental frequency (F0): in some conditions identification accuracy is improved by shifting the formant frequencies (FFs) and F0 in the same direction, compared to conditions where one is raised and the other is lowered. The results indicate the presence of perceptual mechanisms that are sensitive to the natural covariation of F0 and FFs in human voices. Initial modeling shows that including F0 and FFs predicts listeners’ behavior better than FFs alone. Specifically, posterior probabilities from linear discriminant function analysis are better correlated with listeners’ identification rates when F0 is included than when it is not. Further modeling suggests prediction of overall perceptual results generally improves (especially in mismatched conditions) for modified models that include a positive correlation between F0 and FF that is somewhat weaker than that observed in natural speech databases. [Work supported by NSF and SSHRC].
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".