Perceptual Benefits of Linguistic Diversity and Language Background: Evidence from Auditory Free Classification of English Dialect Accents and Asian-Accented English
Bibliographic record
Abstract
Non-linguistic factors leave a distinct thumbprint on our speech production that is perceptible to listeners. A steadily growing line of research demonstrates that listeners can perceive a contrast between native and non-native (L2) speakers based on accents and further classify these speakers according to dialectal variation, even when they are not native speakers of a language. Most of these studies have focused on dialectal variation within US English speakers, a combination of US and International English dialects, or L2 speakers representing a wide range of languages. Most have also featured listeners who are monolingual native speakers of the target language coming from a homogenous background, or a contrast between these and a targeted set of L2 speakers. We therefore lack knowledge of how exposure to, or familiarity with, diverse accents and languages, or specific native language competence of the native language of L2 speakers, can guide listeners’ accent perception and categorization. In this research, we employed a free classification task, presenting listeners with speech samples of native speakers with accents representing multiple English dialects, and L2 speakers of nine Asian languages across three geographic regions speaking Asian-accented English. There were six groups of listeners: monolingual US English listeners in a diverse linguistic context, monolingual US English listeners in a homogeneous linguistic context, native speakers of a non-Asian language and English (bilinguals), and native speakers of each of the three target Asian language groups who are L2 speakers of English. The results reveal that nearly all listeners are sensitive to accents capturing native/L2 contrasts and dialectal variation in English. While regular exposure to a diversity of accents results in increased classification accuracy, classification of Asian L2-accented English speakers is best performed when there is alignment of similar language family and geographic area, as demonstrated by South Asian listeners.
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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 | 0.035 |
| 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 | 0.001 |
| 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".