The influence of linguistic and musical experience on Cantonese word learning
Bibliographic record
Abstract
Adult non-native speech perception is subject to influence from multiple factors, including linguistic and extralinguistic experience such as musical training. The present research examines how linguistic and musical factors influence non-native word identification and lexical tone perception. Groups of native tone language (Thai) and non-tone language listeners (English), each subdivided into musician and non-musician groups, engaged in Cantonese tone word training. Participants learned to identify words minimally distinguished by five Cantonese tones during training, also completing musical aptitude and phonemic tone identification tasks. First, the findings suggest that either musical experience or a tone language background leads to significantly better non-native word learning proficiency, as compared to those with neither musical training nor tone language experience. Moreover, the combination of tone language and musical experience did not provide an additional advantage for Thai musicians above and beyond either experience alone. Musicianship was found to be more advantageous than a tone language background for tone identification. Finally, tone identification and musical aptitude scores were significantly correlated with word learning success for English but not Thai listeners. These findings point to a dynamic influence of musical and linguistic experience, both at the tone dentification level and at the word learning stage.
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".