Pattern Learning and Accent Familiarity in Monolingual and Bilingual English Speakers: Generalizing Learning Across Accented Speech
Bibliographic record
Abstract
It is increasingly common to encounter speakers with an accented variety of English, especially as society becomes more diverse and multilingual. To begin shedding light on how to improve communication outcomes when interacting with a speaker who has an accent, this research investigates individual cognitive factors which may enhance the ability to process accented speech. These factors include statistical learning ability, accent familiarity, language background, and the relationships therein. Statistical learning is the ability to extract regularities from the environment and is highly implicated in processing language. A novel auditory statistical learning (aSL) task with accented phonemes was administered to 43 participants recruited from the University of Alberta, alongside a visual statistical learning task, language background questionnaire, and accent familiarity questionnaire. Linear regression analysis was used to examine the differences in performance between English L1 and L2 participants on the aSL task. It was found that the English L1 participants had a faster reaction time overall, though the English L2 participants were more correct overall. Accent familiarity was a significant predictor of reaction time, especially for L2 participants. Performance on the visual statistical learning task was the only significant predictor of overall aSL score; however, more nuanced analysis showed differences in performance by question type between the two groups. The findings contribute to understanding how bilingualism can affect the processing of accented phonemes, how statistical learning and bilingualism are related, and how exposure to a variety of accents may be just as beneficial as exposure to a particular accent when processing accented speech. This thesis also provides a lasting contribution to the field by providing a new auditory statistical learning task and Accent Familiarity Questionnaire with preliminary reliability and validity. It also provides an essential foundation for future research.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".