Comparison of Pitch Accent in Native Speakers of English & Mandarin Learners of English
Bibliographic record
Abstract
This research project was conducted as a pilot study to explore how pitch accent is used by NCES (Native Canadian English Speakers) and MLE (Mandarin Learners of English). Pitch accents are the prominent high or low tones, that are predominantly found on content words (N, V, Adv, Adj, etc.) in English. In order to compare how both speech communities use pitch accent in English, participants were given an EI (Elicited Imitation) Task. The EI involved participants hearing and then repeating a sentence. It is also reconstructive in nature meaning that the participants process the sentence, then reconstruct it with their own grammar, and finally reproduce it. The results showed that Mandarin speakers had more pitch accents than English speakers, adding pitch accents on function words (Art, Pro, Prep, etc.) as well. The results also demonstrated that Mandarin speakers had less creaky words (words said in a very low pitch, also known as laryngealization or vocal fry) than the English participants. Implications of this study concern ESL Education; such as should English pitch accent patterns and creak in English be taught to English language learners.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 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) | 0.002 | 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".