The effect of reduction and orthographic consistency in an auditory repetition task
Bibliographic record
Abstract
Reduction, the shortening, warping, or deletion of speech sounds, is a natural process in speech communication, especially in casual or fast speech. While reduced words are easier to produce, they require more perceptual effort. Also, words with consistently spelled sounds are understood faster and more accurately. Taking reduction and consistency together, we hypothesize that reduction is more likely in words with consistent written forms. Unlike English, Japanese implements logographic orthography, in which written symbols represent an entire word or concept, rather than individual sounds. The present study analyzes production data from a delayed repetition task previously collected by Mukai et al. (2023) and examines the relationship between orthographic consistency and reduction among Japanese speakers. We also compare participants’ productions to the speech stimuli. Our results indicate that speakers align reduction characteristics in the target speech: the duration of reduced targets are shorter than the unreduced counterparts. We also find a consistency effect in which speakers decrease the duration of unreduced targets as the consistency increases. The results are discussed regarding their implications for how listeners align their production when listening to reduced speech and the role of the orthographic form in speech processing.
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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 | 0.002 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".