The timing of Accentual Phrases in read and spontaneous speech: data from Acadian French
Bibliographic record
Abstract
While comparisons of read and spontaneous speech materials note prosodic differences such as the number and position of pauses and the number and position of tone unit boundaries, only a few studies have examined the rhythm of these two speech styles. In these studies, rhythm is measured with metrics based on the durations of segmental – that is, vocalic and consonantal – intervals. In this paper we consider the timing of larger units known as Accentual Phrases (APs), which are groups of syllables that are demarcated by a primary stress. Our study uses AP-based rhythm metrics to examine differences between read and spontaneous speech materials. Data are from sociolinguistic interviews conducted with 12 speakers of a variety of Acadian French spoken in northeastern New Brunswick. Each speaker produced both reading and spontaneous styles. Approximately 34 minutes of speech – about 2,400 APs – were analyzed. APs were identified by two native speakers. Calculations of AP-based rhythm metrics were made with the same equations that are used for segmental interval measures (delta-AP, Varco-AP) and for segmental pairwise variability measures (nPVI-AP, rPVI-AP). APs in spontaneous speech are shorter in duration than those in read speech, even though the average number of syllables per AP is similar in both styles. APs in spontaneous speech also have greater durational variability, and they show greater inter-speaker variation. Discrimination analysis suggests that the normalized metrics –Varco-AP, nPVI-AP – contribute most to distinguishing between the two styles. One implication of this study is that AP-based rhythm metrics can contribute to a framework for the comparison of read and spontaneous speech materials. More generally, the study confirms that there are interesting patterns of speech timing that are located at a level above vocalic and consonantal segments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".