Acoustic Manifestation of English Lexical Stress Pattern by Native Erei Speakers
Bibliographic record
Abstract
Lexical stress is the combination of intensity, fundamental frequency and vowel quality acoustically. Like many other non-segmental features of English, it is very vital for intelligibility, foreign accentedness and comprehensibility since wrong placement of primary and/or secondary stress in English words might lead to different interpretations. The feature is not observable in Erei, which is a tonal language, where all the syllables or vowels in a word are given strong form. Erei language is different from free variable stress system of English, and the difference between the two languages may likely result in the transfer of Erei tonal system in the articulation of English lexical stress by native Erei speakers. The study examined the deployment of English lexical stress in the speech outputs of Erei-English bilingual speakers in Biase Local Government Area of Cross River State Nigeria. Eight subjects were selected from four secondary schools. Eight words, selected from Cruttenden’s Gimson’s pronunciation of English, were used for the analysis. The metrical theory, developed by Lieberman (1975), was adopted as the framework for the analysis. Findings indicated that Erei-English bilinguals place stress on the wrong syllables as shown in the native British speaker’s output, and therefore, do not observe English rhythmic alternation rules. All the syllables in a word are almost given equal prominence, a rehash of the tonal nature of Erei, affecting the intelligibility of their spoken English. Based on the findings, the study suggested the availability of well-equipped language laboratories, provision of sophisticated audio-visual aids and computerised speech equipment in Nigeria as well as language teachers in L2 situations should focus instruction on non-segmental features before the individual segments to promote international intelligibility in the speech outputs of L2 users.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.001 | 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".