The Effect of Ethnicity on Identification of Korean American Speech
Bibliographic record
Abstract
Research on ethnic varieties of American English has found that listeners can identify speaker ethnicity from voice alone at above-chance rates. This study aims to extend this research by focusing on the perception of race and ethnicity in the voices of ethnically Korean speakers of English. Bilingual Korean Americans in California provided samples of English speech that were rated by 105 listeners. Listeners rated the speakers on their likelihood of being a certain race or ethnicity (including Asian and White). Listeners who were Korean themselves rated the speakers as more likely to be Asian and Korean, whereas non-Asian listeners rated the speakers as more likely to be White. Non-Asian listeners also demonstrated a negative correlation between rating a voice as Asian and rating a voice as belonging to a native-born American, while Asian listeners did not. Finally, a positive correlation between pitch and perceived Asianness was found for female speakers, corresponding to listeners’ metalinguistic commentary about the hallmarks and stereotypes of Asian or Asian American speech. The findings implicate the listener’s own ethnic identity and familiarity with a speech variety as an important factor in sociolinguistic 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 | 0.002 | 0.007 |
| 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 | 0.000 |
| 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".