Assessing retroflex and alveolar liquid perception and production in heritage Tamil speakers
Bibliographic record
Abstract
In this presentation, we explore factors affecting and the relationship between perception and production of the alveolar-retroflex liquid contrast ([l]-[É]) in heritage Tamil speakers. In particular, we examine the connection between language and identity in the maintenance of this acoustically fragile contrast with similar spectral characteristics, shown through low perceptual salience. Tamil speakers (n = 18) completed an AX discrimination task with non-word Tamil VCVs was administered. D-prime, a bias free measure of perceptual distance, was computed from the discrimination data. Additionally, participants provided minimal pairs with the target consonants in an elicited production task. An F3-F2 (Hz) score was taken as a measure of productive salience, with alveolars having a larger difference than retroflexes. Quantitative results showed a high degree of variation in productive salience, as some speakers clearly produce the contrast while others did not. Perceptual distance was also variable, with some participants clearly showing categorical discrimination while others did not. Qualitative results revealed that a concrete ties to tangible culture and a strong linguistic identity can serve as an indicator of accuracy in perception and production. This research addresses whether acoustically fragile contrasts are realized in heritage Tamil and provides new insight into heritage phonological contrast retention and maintenance.
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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 | 0.000 | 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".