Predictability effects on vowel realization in spontaneous speech
Bibliographic record
Abstract
Previous research on vowel realizations within the formant space has found effects for lexical factors such as word frequency in both laboratory settings (Wright, 2004; Munson & Solomon, 2004; and others) and in spontaneous speech (Gahl, Yao, & Johnson, 2012). In addition to lexical factors, semantic context has also been found to influence vowel realizations in laboratory settings, such as emphatic/non-emphatic contexts (Fox & Jacewicz, 2009) and whether a word is predictable from the preceding words (Clopper & Pierrehumbert, 2008). The current project looks at whether effects on vowel realization for semantic context from the laboratory can be extended to spontaneous speech. As in Gahl, Yao, and Johnson (2012), the Buckeye Corpus (Pitt et al., 2007) will be used, with the same predictors used there with the addition of a semantic predictability measure. Semantic predictability for a given word will be calculated based on relatedness of that word to words five seconds before the word or less, where relatedness will be calculated based on WordNet (Princeton University, accessed 2012). As a listener can rely more on context for disambiguation, words that are predictable from their preceding context are hypothesized to contain less distinct vowels than words that are not predictable from context.
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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 | 0.014 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.004 | 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".