Interaction of cues to vowel identity and consonant voicing: Cross-language perception
Bibliographic record
Abstract
Canadian English has two high front vowels differing in spectral and duration properties, Spanish has one high front vowel, and Japanese has two high front vowels differing in duration only. Vowel duration is a major cue to post-vocalic consonant voicing in English, but not in Japanese or Spanish. Canadian English, Japanese, and Mexican Spanish listeners identified members of a multidimensional edited speech continuum covering the English words bit, beat, bid, bead. The continuum was created by systematically varying the spectral properties of the vowel, and the durations of the vowel, the consonant closure, and the carrier sentence. English listeners had a categorical cutoff between /i/ and /I/ based primarily on the spectral properties of the vowel. Half the English listeners identified consonant voicing using vowel duration. Japanese listeners had a categorical cutoff between the English vowels based primarily on the duration of the vowel. The location of the cutoff was the same as the categorical cutoff between Japanese long /i:/ and short /i/. Japanese listeners identified consonant voicing at random. Spanish listeners identified the English vowels using vowel duration but did not have a categorical cutoff. Half the Spanish listeners identified consonant voicing using the spectral properties of the vowel.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".