Why do non-native speakers have a foreign accent? A three-dimensional perspective
Bibliographic record
Abstract
A three-dimensional perspective, following the speech-chain model, is taken in arriving at the variables that influence the production and perception of foreign-accented speech. Essentially, research to date indicates the interactive role of all three communication components of the speech-chain model. First, speech-related variables, i.e., the interlanguage differences in the phonetic patterns of the speech, of L2 speakers compared to the L1 speech patterns influence listeners perception of accentedness of non-native speech. Second, speaker-related variables (i.e., differences in age, other psychological variables) cause the non-native speakers to have difficulties in learning to map new sounds of the L2 onto their existing L1 phonetic system, thus resulting in foreign-accented speech patterns. Lastly, differences in listener-related factors (i.e., L1 of the listener, prior linguistic experience, amount of exposure, listening-conditions in which they hear the accented speech) have been found to influence the perception of foreign-accentedness of speech. Past and current findings will be brought to bear upon this issue that has implications in theoretical understanding of speech perception, as well as practical applications in accent-modification and ESL classroom training programs.
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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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".