Language Experience Influences Sociolinguistic Development: The Role of Speaker Race and Language Attitudes on Bilingual and Monolingual Adults’ Accent Processing
Bibliographic record
Abstract
BACKGROUND/OBJECTIVES: Speaker race and the listener's language experience (i.e., monolinguals vs. bilinguals) have both been shown to influence accent intelligibility independently. Speaker race specifically is thought to be informed by learned experiences (exemplar model) or individual biases and attitudes (bias-based model). The current study investigates speaker race and the listener's language experience simultaneously as well as listeners' attitudes toward non-native speakers and their ability to identify the accent. METHODS: Overall, 140 White English monolinguals and 140 English/Norwegian bilinguals transcribed 60 Mandarin-accented English sentences presented in noise in the context of a White or East Asian face. Following sentence transcription, participants were asked to rate the strength of the accent heard and completed a short questionnaire that assessed their accent identification ability and their language usage, proficiency, familiarity, and attitudes. RESULTS: Results show that a listeners' ability to identify an accent and their attitudes toward non-native speakers had a significant impact on accent intelligibility and accentedness ratings. Speaker race by itself did not play a role in accent intelligibility and accentedness ratings; however, we found evidence that speaker race interacted with participants' accent identification scores and attitudes toward non-native speakers, and these interactions differed as a function of language experience. CONCLUSIONS: Our results suggest that bilinguals' sociolinguistic processing may be more in line with a bias-based model than monolinguals.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".