Does non-native language influence learning a melody? A comparison of native English and native Chinese university students on the AIRS Test Battery of Singing Skills
Bibliographic record
Abstract
The AIRS Test Battery of Singing Skills (ATBSS) is a protocol for acquiring data about a wide range of singing abilities, one of which is the ability to learn an unfamiliar song. This test component presents a song with English lyrics. We examined the role of native language of the singer in this task by comparing the sung productions of the song by native English versus native Chinese students. Following a pilot study that suggested the impact of native language, in the main experiment, 12 participants (6 from each language group) heard the test song with lyrics in their native language (English/Mandarin Chinese), and 12 more heard the lyrics in the foreign language. The instructions were to sing “la” and not the lyrics. For both native-language groups, those who heard the song in their native language made fewer melodic contour errors. When later asked to sing the song with lyrics, the benefit of native language was also evident for both contour and lyrics. These effects of degree of matching of one’s native language with the language of the lyrics when learning a new song emphasize the importance of controlling for native language when recruiting for the ATBSS. It also validates efforts to create versions of the ATBSS in prominent world languages.
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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 | 0.005 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".