A new non-linear regression model for formant trajectories in English monosyllables incorporating dual targets for vowels.
Bibliographic record
Abstract
A new non-linear regression model is proposed to characterize the formant trajectories of the vocalic portion of English CVC syllables in the data described by Hillenbrand et al. [J. Acoust. Soc. Am. 109, 748–763 (2001)]. The modeling framework builds on work of Broad and Clermont [J. Acoust. Soc. Ame. 81, 155–165 (1987)], wherein formant trajectories were modeled via three additive components: (1) a single vowel target, (2) an exponential approach (in time from onset) toward the vowel target from an initial consonant onset value, and (3) an exponential approach (in time from offset) toward the vowel target from a final consonant offset value. The new model extends this to allow for a dual specification (nucleus + offglide) of the vowel targets [T. Nearey and P. Assmann, J. Acoust. Soc. Am. 80, 1297–1308 (1986)]. Initial results suggests that inclusion of a second vowel target provides substantial reduction (about 40%, 23% and 7% respectively for F1, F2, and F3) of error variance on trajectories averaged across 12 speakers. More detailed statistical analyses of variants of the new model are underway and will be reported for both the data described above and that reported on by Assmann et al. [this meeting].
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".