Influence of /r/ on burst spectra for stop place
Bibliographic record
Abstract
Bilabial and lingua-alveolar stops in 20 minimally contrastive word pairs in both singleton and stop /r/ (cluster) contexts were recorded from two girls, one with facial paralysis (CFP) and one with normal facial movement (CNM). Auditory identification of these productions by 12 listeners revealed a significant place by context interaction for CFP. Identification scores were high for her lingua-alveolar stops (99.3% singletons; 94.8% clusters). Identification scores for her bilabial stops were lower, with singletons being significantly lower (57.8%) than clusters (77.8%). Acoustic cues for stop place (F2 onset frequency, VOT, mean and skewness of burst spectra) were measured for all word productions. For both girls, F2 onset and VOT measures were lower for bilabial than lingua alveolar stops in singletons and clusters. CFP’s burst spectra for bilabials had a higher mean and more negative skewness than lingua alveolars in singletons and clusters. CNM’s burst spectra for bilabials had a lower mean and more positive skewness than lingua alveolars in singletons (expected) but a higher mean and more negative skewness for bilabials than lingua alveolars in clusters (unexpected). This unexpected finding may account for CFP’s higher identification rates for bilabial clusters (all acoustic cues ‘‘fit’’ those of a child with normal facial movement).
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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".