Bibliographic record
Abstract
The present study reports preliminary results of an experimental evaluation of the stability of rhythm metric scores across different speech genres (i.e., read sentences vs. spontaneous speech) in English and Japanese. Data for native English (L1 English) speakers and from one L2 English speaker (L1 Japanese) was extracted from the Wildcat Corpus (Van Engen et al., 2010). L2 English and L1 Japanese data was extracted from unpublished data generated in an active project investigating spontaneous speech across dialects and accents (Warner et al., 2015). Three metrics (i.e., %V, VarcoV, and nPVI_V) were employed to quantify durational characteristics of the total 48 spontaneous utterances (L1 English: 3 speakers x 3 utterances, L1 Japanese: 3 speakers x 3 utterances, L2 English: 6 speakers x 5 utterances). The rhythm metric scores from the spontaneous corpora were compared to the results in Grenon and White (2008) who examined English and Japanese speech rhythm with 90 read sentences. We predict that measures of rhythm will differentiate between read and spontaneous speech, with spontaneous speech falling on the faster side of each metric. While Grennon and White (2008) had difficulty distinguishing between L1 Japanese and L2 English speech, we predict that using more natural speech will allow for better discrimination of these two speech groups. As predicted, the spontaneous speech had a slightly lower %V scores and a slightly higher VarcoV scores as compared to the read speech of Grenon and White (2008). However, we found that the speech rate of the spontaneous speech was slower than the read speech. The results suggest that metric scores pattern in a similar way across of speech genres, but that the spontaneous speech better distinguishes the L1 Japanese from the L2 English.
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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 | 0.013 |
| 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 | 0.000 |
| 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 | 0.000 |
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".