A modified statistical pattern recognition approach to measuring the crosslinguistic similarity of Mandarin and English vowels
Bibliographic record
Abstract
This study describes a statistical approach to measuring crosslinguistic vowel similarity and assesses its efficacy in predicting L2 learner behavior. In the first experiment, using linear discriminant analysis, relevant acoustic variables from vowel productions of L1 Mandarin and L1 English speakers were used to train a statistical pattern recognition model that simultaneously comprised both Mandarin and English vowel categories. The resulting model was then used to determine what categories novel Mandarin and English vowel productions most resembled. The extent to which novel cases were classified as members of a competing language category provided a means for assessing the crosslinguistic similarity of Mandarin and English vowels. In a second experiment, L2 English learners imitated English vowels produced by a native speaker of English. The statistically defined similarity between Mandarin and English vowels quite accurately predicted L2 learner behavior; the English vowel elicitation stimuli deemed most similar to Mandarin vowels were more likely to elicit L2 productions that were recognized as a Mandarin category; English stimuli that were less similar to Mandarin vowels were more likely to elicit L2 productions that were recognized as new or emerging categories.
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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 | 0.002 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".