Manner-specific tongue shape differences in the production of Kannada coronal consonants
Bibliographic record
Abstract
The production of consonants of the same place but different manner of articulation can involve certain adjustments in the posture of the tongue shape. This can be due to requirements for specific gestures (e.g., lowering the tongue sides for laterals) or constraints on coordination of different gestures (e.g., the tongue-palate constriction and the velum lowering for nasals). This study used ultrasound imaging to examine sagittal tongue shape differences in the production of Kannada (Dravidian) laterals, nasals, and stops of two places of articulation—alveolar/dental and retroflex. Words with these consonants (as geminates) were produced multiple times by five female and five male native speakers of Kannada. The analysis of tongue shapes revealed a lower tongue body/blade for laterals than stops, but only in retroflexes. The opposite was observed for /l/ vs. /t/, likely reflecting the alveolar vs. dental constriction differences. The nasals were produced with a significantly more advanced tongue body than the corresponding laterals and stops. The tongue fronting for nasals can serve to accommodate the velum lowering as part of these consonants' gestural coordination. The magnitude of this effect is further modulated by the consonant’s degree of articulatory resistance, which is greater for retroflexes than alveolars/dentals.
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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 | 0.002 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".