The Role of Temporal and Spectral Cues in Non-native Speech Production:
Bibliographic record
Abstract
This study investigated the role of durational and spectral cues in second language tense and lax vowel contrasts produced by non-native speakers. To test previous claims that speakers primarily rely on durational cues over spectral cues to distinguish L2 tense and lax vowel pairs, citation style speech data were collected from 16 native speakers of Bangla; participants were all undergraduate students. The data were collected via a shadowing task where participants listened to a carefully constructed list of English words in random order and repeated each word immediately after they heard them. The utterances were recorded via a Zoom H1n voice recorder. Collected speech data were annotated and processed using the phonetic analysis software Praat and the semi-automatic annotation toolkit DARLA; statistical analyses were performed using R statistical computing software. Results indicate that Bangla speakers do not emphasize on durational cues to differentiate English tense-lax vowel pairs, contrary to the general patterns reported from other languages; rather, they prefer the spectral cues over the durational cues.
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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 | 0.001 | 0.006 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".