Speaker and listener variables affecting second language vowel intelligibility.
Bibliographic record
Abstract
Research investigating the development of second language (L2) phonology often relies on native speaker evaluation of L2 productions. However, listener variables that might affect these judgments remain little understood. For example, Levi et al. (2007) argued that the lexical frequency of L2 speech tokens influences listeners. However, they used somewhat incommensurate measures to compare listener vis-a-vis speaker variables. The present study investigates the impact of speaker and listener variables on English vowel intelligibility using three distinct listening tasks and compares three speaker groups: first language (L1) English, L1 Mandarin, and L1 Slavic. Speakers repeated a word list comprising 10 target English vowels, each embedded in three separate monosyllabic verbs and varying in terms of lexical familiarity for speakers and lexical frequency for listeners. L1 English judges identified the recorded vowels in two conditions that included lexical information, and one condition in which the vowel portions of the recorded words were presented in isolation, preventing listener reference to the vowels’ lexical context. Results indicate an interaction between lexical familiarity for speakers, speaking prompt type, and intelligibility scores. Lexical frequency for listeners did not impact intelligibility scores.
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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 | 0.013 |
| 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 | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".