Bibliographic record
Abstract
Is it more difficult to detect an accent when someone is singing than when they are speaking?Previous work on accents in song has focused on professional singers, who modify their accents when they perform music that is associated with a particular regional accent (Trudgill, 1983, Simpson, 1999, O'Hanlon, 2006, Gibson, 2010).We ask whether there is something about singing per se that causes a shift in accent.In order to answer this question, our study differs from previous work in two important ways: 1) We do not make use of professional singers in our study, and 2) the music in our study is not culturally associated with a particular country.We recorded twelve speakers: six native speakers of English, and six second-language speakers.They were asked to sing "Twinkle Twinkle Little Star" and read a passage from "Goldilocks".Forty native English listeners had more difficulty detecting a foreign accent in the singing conditions and rated speakers as having less of a foreign accent in song compared to speech.These results suggest that it is more difficult to detect a foreign accent in song compared to speech even when the singers are not influenced by an accent associated with a particular genre.An analysis of the recordings showed that vowel duration is generally longer in song and that the pitch track changes in song.However, there is no clear and consistent difference in how vowels are pronounced.Based on these findings, we argue that accents are more difficult to detect in song than in speech because the rhythm and pitch of song mask important prosodic markers of accent.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".