An MRI-based articulatory characterization of Kannada coronal consonant contrasts
Bibliographic record
Abstract
This paper investigates production mechanisms of a complex set of coronal consonant contrasts in Kannada, with the goals of (i) testing previous theoretical distinctive feature approaches to coronals and (ii) documenting this relatively phonetically under-studied Dravidian language. An extensive corpus of articulations was collected by static MRI midsagittal from two female speakers, including a full set of Kannada place and manner contrasts in five vowel contexts. Articulatory modelling was used to determine a small set of components responsible for the implementation of various consonant classes, including the place sets of dentals, retroflexes, and alveolopalatals, and manner sets of stops, fricatives, nasals, liquids, and glides. Overlaid average contours were used for the traditional phonetic classification of consonants, while dispersion ellipses were employed to capture consonants' susceptibility to vowel coarticulation. The results provide evidence for some of the distinctive feature characterizations of coronals (e.g. constriction location and tongue body height distinctions for dentals and retroflexes), while disconfirming others (tongue dorsum backing for retroflexes). They also largely confirm previous descriptive phonetic accounts of Kannada consonants (including the merger of retroflex and alveolopalatal sibilants), while at the same time identifying somewhat unexpected articulatory patterns (such as dorsal constrictions for /ʋ/, /h/, and /r/). All the images and articulatory contours are made publicly available.
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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 | 0.001 | 0.001 |
| 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".