Bibliographic record
Abstract
It is well-attested that linguistic experience affects the perception of non-native sounds. The vast majority of research on L2 perception has been carried out on segmental features from the perspective of phonetic similarity between the L1 and L2 sound systems. The general goal of this study is to expand our understanding of cross-linguistic comparison in non-native speech perception, focusing on the somewhat understudied area of perceptual assimilation at a suprasegmental level. More specifically, it deals with comparisons of prosodic systems in two tone languages, Mandarin and Cantonese. The current study examined perception of Mandarin tones by Cantonese listeners. In Experiment 1, Mandarin tones were presented and the Cantonese listeners were requested to identify which tone they heard. In Experiment 2, the Cantonese listeners were instructed to rate how similar each Mandarin tone was to a Cantonese tone. Preliminary results suggest that tonal confusion errors may result from not only the similar acoustic properties of the tone pairs but also perceptual assimilation between L1 and L2 tonal contrasts. These findings are discussed in terms of the effects of L1 prosodic system on L2 perception and how perceptually assimilation patterns predict listeners’ perception performance at the domain of lexical tones.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".