Detecting Foreign Accents in Song
Bibliographic record
Abstract
This paper presents three experiments exploring the perception and production of accents in song. In a perception experiment, participants listened to passages sung and spoken by native and non-native speakers of English. The participants did better at identifying native speakers when listening to the spoken passages. Accents were also judged as more native-like in song than in speech. In addition, two production experiments compared the acoustic characteristics (pitch, duration, F1 and F2) of sung and spoken vowels, produced by native and non-native speakers of English. Both native and non-native speakers changed the pitch and duration of their vowels when singing; the vowel quality was not consistently shifted. Together, the results indicate that the melody imposed by the song impacts the suprasegmental properties of pronunciation whereas the segmental properties remain largely intact. Based on these results, we conclude that a main reason why accents are more difficult to detect in song than in speech is that the rhythm and melody imposed by the song mask intonational cues to 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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".