MODELING PERCEPTIONS OF THE ACCENTEDNESS ANDCOMPREHENSIBILITY OF L2 SPEECH The Role of Speaking Rate
Bibliographic record
Abstract
In much previous research, listeners' rating data have served as a dependent variable to demonstrate the effects of age of learning, length of residence, and motivation on L2 users' degree of foreign accent. However, the role of speaking rate in such judgments has not been ascertained. To gain new insight into this relationship, we carried out two experiments involving sentence-length utterances produced by English L2 users. In the first, we observed a significant curvilinear relationship between speaking rates and accentedness and comprehensibility judgments of utterances produced by users from a variety of L1 backgrounds. In the second experiment, by manipulating rates with speech compression-expansion software, we established that this effect was due to the rate differences themselves, rather than to differences in L2 proficiency that might co-vary with rate. In both experiments the listeners tended to assign the highest ratings to L2 speech that was somewhat faster than the rates generally used by L2 users; however, both very fast and very slow speech tended to be less highly rated. Researchers who use listener rating data should be mindful of the potential confounding effect of speaking rate in their data.
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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 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".