Sonority in British English
Bibliographic record
Abstract
Our previous study on British English speech [Nakajima et al.; Fechner Day (2012), Ottawa] was extended into the domain of phonology. Factor analyses were performed on the power fluctuations of the outputs of 19 critical-band filters, which separated British English sentences uttered by two female speakers and one male speaker into narrow-band signals. A database of British English spoken sentences [The ATR British English speech database (Campbell, 1993)] was used for the present analysis, because each identifiable speech sound was indicated in the speech waveform with a label of a phonetic symbol. About 80% of 31,663 labels were considered to represent English phonemes. Three factors appeared as in our previous research [Ueda et al.; Fechner Day (2010), Padova], and one of them corresponding to a frequency range of about 600–1800 Hz was closely related to sonority or aperture described in linguistics literature. The acoustic sonority could be related to a few phonological phenomena: (1) A sonorant consonant immediately after an obstruent can be a syllable nucleus, (2) a consonant cluster at the beginning of a word mostly begins with an obstruent, and (3) a short schwa cannot be a nucleus of a stressed syllable.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".