A comparison of algorithms to automatically extract vowel formants and nasal poles from tagged vowels in <scp>PRAAT</scp>.
Bibliographic record
Abstract
The goal of the present research was to develop an easy-to-use PRAAT script yielding robust measurements of acoustic correlates of vowel quality and vowel nasality. Several algorithms were scripted in PRAAT and evaluated on two corpora. The first was the Hillenbrand corpus, containing /hVd/ words in isolation, spoken by 50 men, 50 women, 29 boys, and 21 girls, where vowels have been hand-tagged by experts. The best performing algorithm yielded extremely high correlations with the expert-measured F1 and F2 frequencies (r>0.95). The second corpus consisted of spontaneous speech by American English and Canadian French women, addressing either their infant or an adult, and where several types of vowels had been hand-tagged (including point vowels /i,a,u/, frequently used to assess vowel space size; and contrasts of vowel tenseness, likely involving F1 and F2 frequencies; and vowel nasality, correlated with F1 bandwidth, as well as its amplitude in relation to the nasal poles P0 and P1). Although, even the best algorithm yielded spurious F2 values for infant-directed /u/'s, measures were robust indicators of vowel quality and nasality in non-back vowels across the different speakers, languages, and registers. Thus, this script may be useful, particularly for non-specialists, since it only requires vowel-tagging.
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 | 0.003 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.003 |
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".