Quantifying and evaluating the impact of prosodic differences of foreign-accented English
Bibliographic record
Abstract
The identification and correction of prosodic deviations in second-language speech still poses a significant challenge for computer-aided language learning.With this ultimate goal in mind, the current study compares utterances by Cantonese speakers of Canadian English with those of native English subjects through both acoustic analysis and perceptual evaluation.We aim to find measurable prosodic differences accounting for the perceptual results.Our outcomes indicate, inter alia, that unstressed syllables are relatively longer compared to stressed ones in the Cantonese corpus than in the Canadian English corpus.Furthermore, the correlations of syllabic durations in utterances of one and the same sentence are much higher for Canadian English subjects than for Cantonese speakers.The latter use a similar range of F0, but produce more and longer pitch-accents than Canadian English speakers.In a perception study we found that applying native durations together with F0 contours to the foreign-accented speech led to significantly improved listener judgments of prosodic goodness.Adjustments to duration alone also tended to yield better ratings, though the effect was not statistically significant.When durations of native English utterances were adjusted to those of Cantonese speakers, significant decrements in ratings were observed.
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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 | 0.003 |
| 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 | 0.000 |
| 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".