Effects of the delivery on the acoustic and temporal parameters of speech
Bibliographic record
Abstract
This study treats the effects of speech rate in a second language. Six speakers from different geographical areas have produced sentences in written Arabic, carrying fricatives (Arabic specifications), at different speeds of elocution. The selected speakers are: two Lebanese (CH and LI), two inhabitants of Algiers (FE and MA) and two Kabyles (SAand NA, in northern east of Algiers). The purpose of this study is to highlight the compensatory strategies adopted by each speaker with flow constraint. The study carried on the temporal and frequential (formantic) analysis of CVC that are extracted from the corpus sentences, has shown differences on the level of the temporal model (relative durations of CVC compared to the sentence duration) and frequential model (formants values Fl and F2 of the vowel of the considered CVC), in fast flow. These differences can be related to the spoke of the speakers, such as: - Fixed temporal models with variable frequential models and a total centralization of the vowels for the Lebanese speakers (CH and LI). The compensation is made only on the frequential level (one compensation), which could make think of a good motor control in flow constraint. - Variable temporal models with constant frequential models and very little centralization, for Algiers speakers (FE and NA). The compensation is only made on the temporal level (one compensation), which could make think of less smooth motor control than CH and LI in flow constraint. - Variable temporal and frequential models and little centralization (less than CH and LI and more than FE and NA) for speakers SA and MA. The compensation is made on both frequential and temporal levels (two compensations). This could make think of a relatively bad motor control in flow constraint. The compensatory strategies adopted by these three types of speakers seem to emphasize on the closely dependency to the second language, namely the written Arabic.
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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 | 0.008 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".