LEARNING SECOND LANGUAGE SUPRASEGMENTALS: <i>Effect of L2 Experience on Prosody and Fluency Characteristics of L2 Speech</i>
Bibliographic record
Abstract
This study examines effects of short, medium, and extended second language (L2) experience (3 months, 3 years, and 10 years of United States residence, respectively) on the production of five suprasegmentals (stress timing, peak alignment, speech rate, pause frequency, and pause duration) in six English declarative sentences by 30 adult Korean learners of English and 10 adult native English speakers. Acoustic analyses and listener judgments were used to determine how accurately the suprasegmentals were produced and to what extent they contributed to foreign accent. Results revealed that amount of experience influenced the production of one suprasegmental (stress timing), whereas adult learners' age at the time of first extensive exposure to the L2 (indexed as age of arrival in the United States) influenced the production of others (speech rate, pause frequency, pause duration). Moreover, it was found that suprasegmentals contributed to foreign accent at all levels of experience and that some suprasegmentals (pause duration, speech rate) were more likely to do so than others (stress timing, peak alignment). Overall, results revealed similarities between L2 segmental and suprasegmental learning.This research was partially supported by research grants from the University of Illinois and Brigham Young University. Many thanks are extended to Youngju Hong for her help in testing the Korean participants and to Molly Mack and James E. Flege for their advice throughout this research project. The authors gratefully acknowledge Randall Halter, Elizabeth Gatbonton, and five anonymous SSLA reviewers for their helpful suggestions on earlier drafts of this paper as well as Randall Halter for his invaluable statistical assistance.
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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 | 0.000 | 0.001 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".