Pattern classification analyzes of vowel-inherent spectral change in adults and children.
Bibliographic record
Abstract
This study compares the pattern of time-varying spectral change in a database of vowels spoken in hVd words by adults and children ranging from 5 to 18 years. Measurements of vowel formant frequencies (F1, F2, and F3), mean fundamental frequency, and duration were used to train a pattern classifier to determine the optimum sampling locations for the purpose of vowel classification. A series of linear discriminant analyzes was carried out, using leave-one-out cross validation to classify the test stimuli. These analyzes differed in the temporal location(s) at which the formant frequencies were measured and the number (1, 2, or 3) of sample points. For all age and sex classes, classification accuracy was higher when two samples were used rather than a single frame, with a mean increase in accuracy of 10.8%. Adding a third sample point produced marginal improvement in classification, with less than 1% change overall. The highest classification results were obtained when the initial sample was taken relatively early in the vowel (around the 20% point), while the second sample was taken around the 70% point, with relatively minor variations in classification scores across age and sex categories.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".