Are Serbian and English listeners insensitive to lexical pitch accents in Serbian?
Bibliographic record
Abstract
The paper investigated possible perceptual insensitivity effects in the perception of lexical pitch accents by native and non-native listeners, that is, by Serbian and English listeners, respectively. The objective of the study was to explore which word-prosodic categories listeners used when they were required to contrast and recall sequences of lexical pitch accents. To that effect, Serbian and English listeners performed a Sequence Recall Task (SRT) in which they contrasted pairs of non-words with different Serbian lexical pitch accent types, and recalled the sequences of these non-words under different memory load conditions. Listeners' answers were coded correct and incorrect and the accuracy scores between the groups were compared and analyzed. Both groups had almost identical levels of accuracy and they performed well above chance level on each contrast. Neither group exhibited any effects of perceptual insensitivity to lexical pitch accents. English (non-native) listeners did not differ in their performance from native Serbian listeners, which, contrary to what previous research suggested, implied that one's native language word-prosodic category inventory did not preclude the encoding of non-native word-prosodic categories. Instead, non-native listeners were capable of deploying different prosodic resources such as post-lexical pitch accents to recall the sequences.
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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 | 0.003 |
| 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".