Dialect perception in song versus speech
Bibliographic record
Abstract
Investigations into style-shift in singing led to the proposal of genre-specific musical sociolects [Coupland, 2011, J. of Soc. 15]. Gibson [2019, Univ. of Canterbury Dissertation] demonstrated that style-shift in popular music is automatic. Additionally, Mageau et al. [2019, Phonetica 76] found non-native English speakers have a less perceptible foreign accent while singing than speaking. These findings provide possible behavioral analogues to the differential processing of speech and song. Gibson [2019, UofCanterbury Dissertation] accounts for this using Todd et al’s [2019, Cognition 185] exemplar theory, emphasizing the role of sung and spoken contexts in perception. We investigate the role of musical context in accent identification. 24 participants completed a dialect-identification task of 32 musical clips from two genres with strong sociocultural associations: country and reggae. 16 clips contained the original instrumental-removed vocals, and 16 different clips from the same songs additionally underwent monotonization and rhythm-normalization. Responses to the manipulated stimuli were more varied than the original vocals. Listeners’ judgements may be more closely tied to country and reggae’s sociocultural associations than speech information itself. These findings counter Mageau et al., illuminating a more complex relationship between accent perception, music, and genre. Future work will investigate this for dialect-nonspecific genres and adjust approaches to stimuli manipulation.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".