The role of genre association in Sung Dialect categorization
Bibliographic record
Abstract
Genre-associated sociocultural cues may influence dialect recognition when listening to music. Previous work identifies genre-specific sociolects [Coupland, 2011, J. Socioling. 15]; singers make genre-dependent production changes [Gibson, 2019, U.Canterbury Diss.]; and accent is perceived with greater ease in song compared to speech [Mageau et al., 2019, Phonetica 76]. However, the role of genre associations in dialect categorization has not been sufficiently addressed. Our previous work suggests that genre identification may play a larger role in this than auditory speech cues [Walter et al., 2023, J. Acoust. Soc. Am. 154]. We investigate this further using a dialect-identification task with improved “spoken” stimuli. Participants heard sung clips (original instrumental-free vocals) and manipulated “spoken” clips (instrumental-free vocals, monotonized and rhythm-normalized) from counterbalanced genres with greater ease of vocal isolation: U.S. folk with some intra-genre dialect variation, and blues with strong associations to African American English [De Timmerman et al., 2024, J. Socioling. 28]. A superior vocal remover and lyrical transcripts were implemented. As predicted, in the sung context, participants identified genre-associated dialects consistently for both genres, but slightly more so for blues. Varied responses to the “spoken” context supports that listeners use genre information in musical dialect 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 | 0.001 | 0.004 |
| 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 | 0.000 | 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".