Locus equations estimated form a corpus of running speech
Bibliographic record
Abstract
Locus equations, or the linear relationship between onset and vowel second-formant frequency F2 in terms of slope and y-intercept, have been presented as possible invariant correlates to consonant place of articulation [e.g., Sussman et al. (1998). Behav. Brain Sci. 21, 241–299]. In the current study, formant measurements were extracted from both stressed and unstressed vowels taken from a database of spontaneous and read speech. Locus equations were estimated for several places of articulation of the preceding consonant. In addition, optimal time frames for estimating locus equations are determined with reference to automatic classification of consonant place of articulation as well as vowel identification. Formant frequencies are first measured at multiple time frames—both before and after voicing onset in the case of voiceless plosives—to find the pair of time frames that best estimates place of articulation via discriminant analysis and other classification methods. In addition, locus-equation slopes are compared between stressed and unstressed vowels as well as between spontaneous and read speech samples. In addition, the role of total vowel duration across these contexts is described. The evaluation of several strategies for optimizing the automatic extraction of formant frequencies from running speech are also reported. [Work supported by SSHRC.]
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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".