Linguistic Feature Analyses of Chinese Learners of English and Contributions of Discrete Features to Perceptual Judgment
Bibliographic record
Abstract
This study (a) conducted a feature analysis of the spoken data of Chinese university students in pronunciation, grammar, and discourse, (b) investigated the contributions of the discrete linguistic features to the perceptual ratings on foreign accent, comprehensibility, delivery, and general language use. Ten university learners were selected from the Spoken Corpus of the English of Hong Kong and Mainland Chinese learners (http://corpus.ied.edu.hk/phonetics/), in which two speakers were paired up to conduct a five minutes interview. Three-level analyses were done to investigate Chinese learners’ linguistic features. Forty listeners from four L1 language backgrounds were recruited to rate the speech samples. The results show that strongly negative correlations were found between the production and perceptual rating scores for “omission of consonant(s) in final position” “redundant article ‘the’”, “silent pauses” and “discourse markers,” suggesting that the four features can be perceived and exert strong negative influences on perceptual judgments. Pronunciation rating had the strongest positive correlations with “foreign accentedness”; grammar rating had the strongest positive correlations with “general language use”; discourse rating had the strongest positive correlations with “general delivery”, and “general language use.” Regarding the rating of comprehensibility, “misuse of conjunctions” “redundant article ‘the’”, “silent pauses”, “lengthening”, and “stressing” showed strong negative correlations whereas “filled pauses” had strong positive correlations with it. Regarding the rating of foreign accentedness, strong negative correlations were found between “omission of consonant(s) in final position”, “lengthening”, “discourse markers”, and “stressing” and the rating of “foreign accentedness”.
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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 | 0.001 | 0.006 |
| 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 | 0.000 |
| 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".