An MRI-based articulatory analysis of the Kannada dental-retroflex contrast
Bibliographic record
Abstract
Abstract This paper investigates the production of dental and retroflex stops, fricatives, nasals, and laterals in the Dravidian language Kannada. This is done using articulatory contours extracted from an extensive midsagittal MRI corpus of two female Kannada speakers’ static vocal tract postures intended to capture key aspects of phonemic articulations. Articulatory modelling was used to determine a set of components responsible for the implementation of place and manner contrasts (/t̪ s̪ n̪ l̪/ vs. /ʈ ʂ ɳ ɭ/). These components included both lingual and non-lingual articulatory parameters. Constriction location and length were also determined based on articulatory contours. The results showed that the two speakers produced non-fricative retroflexes with a retracted tongue tip making a constriction behind the alveolar ridge and a characteristic convex tongue shape, yet without a retraction of the posterior portion of the tongue. Apart from the lingual parameters, place differences were also manifested by the vertical position of the larynx (lower for retroflexes). The realisation of the place contrast in sibilant fricatives was different, as /ʂ/ appeared to be produced by both speakers with a laminal alveolopalatal constriction. Manner differences were captured by various non-lingual parameters, yet being also manifested in constriction locations (more anterior for stops). These findings are discussed in the context of previous descriptive and articulatory accounts of dental-retroflex contrasts.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".