Investigating the comprehension and perception of reduced speech with pupillary response
Bibliographic record
Abstract
Spontaneous, casual speech is highly variable, in part due to reduction processes. Listeners handle these reductions in everyday communication; however, these forms present challenges for models of speech perception and lexical processing. Previous research has found that reaction times to reduced word-medial stops are longer, indicating that they are more difficult to process than words with unreduced word-medial stops (Tucker, 2011). The current study examines spoken word processing (as measured by pupil dilation) of reduced and unreduced word-medial stops to determine (a) if the pupillary response to reduced forms corresponds to reaction time results and (b) when in time any differences emerge. Thirty-nine native speakers of North American English completed a listen-and-repeat task in which 80 isolated disyllabic reduced and unreduced word-medial /d/ and /g/ items (40 of each phoneme) were presented. The pupil size data and speech productions are analyzed and will be reported. The results indicate significantly greater pupil dilation for reduced /d/ and /g/. Words containing /d/ elicited greater dilation than those containing /g/, for reduced and unreduced forms. This suggests that, although word-medial stop reduction is frequent in English, an increased processing load is incurred, mirroring previous reaction time 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 | 0.005 |
| 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 | 0.000 | 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".