Analysis of Intonation Patterns of Selected Nigerian Bilingual Educated Speakers of English
Bibliographic record
Abstract
The intelligibility of everyday speech is built on the mastery and the use of appropriate intonation patterns. This makes intonation the music of everyday speech of which its appropriate use has been the final hurdle that the majority of the speakers of English as a Second Language have not crossed. This paper investigated the intonation patterns of the randomly selected 45 bilingual educated speakers of English, from diverse educational backgrounds representing the three senatorial zones in Ebonyi State. A paragraph from Roach (2010) was given to the participants to read. It was recorded and converted to WAVE audio with the use of audio converter. The utterances of interest to the research were extracted with the use of Sony Sound Forge and segmented on a text grid window on Praat. Pierrehumbert’s Auto-segmental Metrical approach to intonation served as the theoretical framework and the transcription was done using ToBI. The study revealed a low level of proficiency in the use and assignment of accurate patterns of intonation in the speeches of the participants. Aside the widely known and commonly used intonation patterns of fall, rise, rise-fall and fall-rise, it was observed that there was the presence of the use of low pitch accent, low boundary tone in the speeches of the participants. Significant inclination towards the use of the falling tone was observed. However, bilingual make-up or educational qualification does not determine appropriate use of intonation patterns. In a bid to, therefore, account for effective communication among educated bilingual speakers of English, more time should be given to the development of this skill using meaningful utterances in context rather than the use of words or sentences in isolation.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".