Manner and place differences in Kannada coronal consonants: Articulatory and acoustic results
Bibliographic record
Abstract
This study investigated articulatory differences in the realization of Kannada coronal consonants of the same place but different manner of articulation. This was done by examining tongue positions and acoustic formant transitions for dentals and retroflexes of three manners of articulation: stops, nasals, and laterals. Ultrasound imaging data collected from ten speakers of the language revealed that the tongue body/root was more forward for the nasal manner of articulation compared to stop and lateral consonants of the same place of articulation. The dental nasal and lateral were also produced with a higher front part of the tongue compared to the dental stop. As a result, the place contrast was greater in magnitude for the stops (being the prototypical dental vs retroflex) than for the nasals and laterals (being apparently alveolar vs retroflex). Acoustic formant transition differences were found to reflect some of the articulatory differences, while also providing evidence for the more dynamic articulation of nasal and lateral retroflexes. Overall, the results of the study shed light on factors underlying manner requirements (aerodynamic or physiological) and how the factors interact with principles of gestural economy/symmetry, providing an empirical baseline for further cross-language investigations and articulation-to-acoustics modeling.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| 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".