Articulatory variations of Mandarin retroflex consonants produced by second language speakers: An electromagnetic articulograph study
Bibliographic record
Abstract
This study investigated articulatory variations in tongue positions and shapes during the production of Mandarin post-alveolar retroflex consonants produced by speakers with varying Mandarin proficiency (native Mandarin speakers, Japanese L2 speakers of Mandarin, and Japanese monolinguals with no knowledge of Mandarin). We aimed to examine (1) whether there are articulatory variations for Mandarin retroflex consonants and (2) if preferred variations differ across groups. Speakers either read aloud or imitated the consonants after hearing them and their tongue positions and shapes were measured by electromagnetic articulography (WAVE, NDI). Results showed that there are multiple articulatory variations for Mandarin retroflex consonants. Native Mandarin speakers produced a concave or convex tongue shape; all Japanese L2 speakers of Mandarin produced a convex tongue shape. In contrast, the majority of Japanese monolinguals imitated the sounds with an entirely different tongue position: bunching their tongues in the middle, which somewhat resembled the “bunched” rhotic in American English. Despite these articulatory variations, productions by most L2 speakers and several monolinguals were successfully identified as retroflex consonants by native Mandarin listeners. These results suggest that L2 speakers may prefer certain articulatory variations and the preference may change depending on proficiency.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| 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) | 0.001 | 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".