The effects of musical experience on linguistic pitch perception: A comparison of Norwegian professional singers and instrumentalists
Bibliographic record
Abstract
Speech prosody and music share tonal attributes well suited for studying cross-domain transfer effects. The present study investigates whether the specific pitch experience acquired by professional singers and instrumentalists transfers to the perception of corresponding prosodic elements in a native and non-native language. Norwegian and Mandarin words with tonal distinctions, together with corresponding hummed tones, were presented dichotically in a forced attention listening test to three groups of native Norwegian listeners: professional singers, professional instrumentalists, and nonmusicians. While instrumentalists and singers were both more accurate (higher percent correct for both ears) than nonmusicians for Mandarin linguistic and hummed tones, only instrumentalists showed positive transfer to corresponding native Norwegian stimuli. Results indicate a pattern of perceiving tonal distinctions that mirrors the pitch experience acquired through professional vocal and instrumental training: Instrumentalists generally appear to rely on an autonomous, categorical pitch strategy, consistent with their primary training with discrete, fixed intervals. Singers, on the other hand, tend to use a similar strategy for nonlinguistic pitch perception, but not in processing native tonal contrasts. Overall, pitch experience acquired through musical training appears to have a positive transfer to non-native speech perception.
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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 | 0.003 |
| 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 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".