To What Extent Does Musical Aptitude Influence Foreign Language Pronunciation Skills? A Multi-Factorial Analysis of Japanese Learners of English
Bibliographic record
Abstract
This study looks at the influence of musical aptitude on learners’ pronunciation abilities in a foreign language. Whilethere have been many studies that have claimed a link between the two (Slevc & Miyake 2006, Milovanov 2010,etc.), some studies suggest that this link may not be as strong as initially thought (Jackendoff, 2009, etc.). This studyexamines the pronunciation abilities of Japanese University students of similar English level and varying musicalaptitude, but conducts more in depth statistical analysis than previous studies by comparing specific musical abilities,such as pitch, loudness, rhythm, tone and timing, with specific problematic pronunciation points, the English sounds/r/, /l/, /v/, [θ] and [ð]. The results of our experimentation indicated a statistically significant correlation betweenmusical timing aptitude and the ability to pronounce r and l sounds, but no other significant correlations, indicatingthat perhaps only specific musical abilities have influence on specific aspects of pronunciation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".