Perception of non-native consonant length in naive English listeners
Bibliographic record
Abstract
English speakers are sensitive to phonetic length (Pickett & Decker, 1960) but do not maintain a phonemic length contrast (Hayes, 2002). This study examines English speakers' ability to discriminate and identify intervocalic consonant length in Finnish non-words. The consonants were manipulated for length and half of the participants were given brief written instruction regarding the Finnish length contrast. In an AX discrimination task, participants responded to increasing contrast ratio gradiently; however, the instructed group performed significantly better than the uninstructed group. Proficiency in any second language aids contrast detection in those receiving no instruction. In a forced-choice identification task, participants showed no evidence of boundary effects; however, the instructed group performed significantly more like native Finnish controls. Again, second language proficiency aids in consonant length detection. The present results indicate that information about and attention to a novel contrast, along with second language experience, aid processing in novice listeners. Given that learners can eventually maintain native-like contrasts, these factors may be influential in the initial formation of L2 phonological representations and support phonetic level of processing (Werker & Tees, 1984), intermediate to non-linguistic acoustic processing and phonemic processing, at which acoustic duration becomes a phonetically relevant cue.
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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 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".