Acquisition of Voice Onset Time for Voiced Plosives of English by Adult Learners of Balochistan
Bibliographic record
Abstract
This study focuses on two experiments conducted with Eastern and Western Balochi speakers. In Eastern Balochi, voiceless stops have aspirated features, but in Western Balochi, they are unaspirated. Eighty-four native speakers of both dialects of Balochi participated in this study. Participants of the first experiment produced words of their L1 in a picture naming task, and those of the second experiment read words in English. VOTs of the L1 and L2 voiced stops elicited from recordings of the productions. Results show that speakers of Western Balochi transfer their L1 negative VOTs to L2 English-voiced stops. However, Eastern Balochi speakers produce English-voiced stops with VOTs, significantly different from their L1 VOTs. Though they could not produce English-voiced stops with native-like accuracy, they produced English stops with significantly longer pre-voicing duration than their L1-voiced stops. Therefore, the study concludes that speakers of those languages with stops with negative VOT ranges face more difficulty acquiring L2 voiced stops of short-lag positive VOTs than those learners whose L1 does not have such stops. The speech learning model is used in this study to analyze all results.
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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 | 0.002 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".