Cross-language perception of Russian plain/palatalized laterals and rhotics.
Bibliographic record
Abstract
A number of studies investigating factors in non-native speech perception have focused on discrimination and identification of English /l/ and /r/ by listeners whose native languages do not have the relevant phonemic contrast. Relatively little work, however, has been done on non-native perception of lateral/rhotic contrasts in other languages and particularly on the perception of palatalized laterals or rhotics. This paper presents results of an AX discrimination experiment where 84 listeners, native speakers of Cantonese, English, Japanese, Korean, Mandarin, and Russian, were presented with stimuli containing intervocalic consonants /l/, /lj/, /r/, and /rj/ produced by a Russian native speaker. The results revealed significant differences in the perception of the lateral/rhotic contrasts across the listener groups, with relatively good discrimination of the contrasts by Korean and English listeners (yet less accurate compared to Russian listeners), and much poorer discrimination by the other groups of non-native listeners. All the groups, including native speakers, performed better with the more acoustically distinct plain /l/ and /r/ contrast than with the palatalized /lj/ and /rj/ contrast. The results suggest that presence or absence of similar native phonemic categories does not fully predict listeners’ performance, underscoring the importance of sub-phonemic gestural/acoustic detail in non-native perception.
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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 | 0.001 | 0.000 |
| 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".