English listeners categorize murmured stops based on aspiration, not prevoicing
Bibliographic record
Abstract
Previous perceptual studies of English stop voicing focus on Voice-Onset Time (VOT). Aspiration is generally subsumed into VOT, yet [1] complicates this, evincing a trading relation between intensity of aspiration noise and VOT. Our study is the first to examine the role of VOT and aspiration in English listeners’ perception of non-native plain and murmured stops. We recorded Marathi talkers producing /CVsV/ nonce words beginning with /t/, /tʰ/, /d/, /dʰ/, e.g. /dʰaːsaː/, or their velar counterparts. The following acoustic measures were taken for each token: — Duration of prevoicing — After Closure Time (ACT) [2], i.e., the interval between release and periodicity — Pre-Vocalic Interval (PVI) [3], which includes ACT and the murmured portion of the vowel — Intensity of aspiration noise Canadian English listeners rated the stops on a 6-point scale from voiced to voiceless. Only results for murmured stops are discussed here. Despite variability of prevoicing duration in these tokens (range = 200ms, sd = 43), the factor did not correlate significantly with a token’s average rating (p>0.60). However, ACT (r=.53, p<0.001), PVI (r=0.39, p<0.001), and the mean intensity of aspiration noise (r = 0.58, p<0.02) did. Thus, aspiration, not prevoicing, best accounts for perceptual differences between murmured stops. [1] Repp, “Relative amplitude of aspiration noise…,” Lang. Speech 22, 1979; [2] Mikuteit & Reetz, “Caught in the ACT…,” Lang. Speech 50, 2007; [3] Berkson, “Capturing breathy voice…,” Kans. Work. Pap. Ling. 33, 2012.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".