Word duration and segment deletion as measures of reduction in a corpus of spontaneous speech.
Bibliographic record
Abstract
The present study explores phonetic reduction in the Buckeye Corpus [Pitt et al., Speech. Commun. 45, 89–95 (2005)] following up on the work of Johnson [Proceedings of the 1st Session 10th International Symposiam (2004), pp.], who counted the number of segments and syllables deleted from each word in the subset of the corpus that was available at the time. The first experiment presented here provides updated rates of segment deletion (over 25% of words) and syllable deletion (over 6% of words) as measures of reduction rates in the entire completed corpus. The second experiment investigates reduction in the duration of words as a function of several linguistic factors, including frequency, conditional probability, and rate of speech. The data are modeled using multiple mixed-effect linear regression to predict both the duration of each word and the reduction from the median duration of each word. Age, gender, and interviewer gender were not found to affect word duration. Citation length and current rate of speech were both strong predictors of word duration. Frequency and predictability from context also correlate strongly with word duration. Verbs were found to be significantly shorter in duration than other content words with the same citation length.
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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 | 0.003 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".