Accentedness in English is connected to inter-word (co)articulation and speech rate, a pilot study
Bibliographic record
Abstract
Previous studies have shown that consonantal errors and difficulties in coarticulation may decrease English language learners’ (ELL) speech intelligibility. The current pilot study investigates connections between inter-word (co)articulation, perceived accentedness, and speech rate. Participants were six adults (2 men) and one native speaker, with L1 of Mandarin (n = 4), Thai (n = 1), and Finnish (n = 1). Methods: The dataset contained 21 repeating (4x) short sentences and two reading passages with all English phonemes. Inter-word consonantal errors, speech rate (compared to the model), and accentedness (2 judges) were compared statistically (Spearman) and qualitatively. Results revealed that accentedness correlated strongly with the number of errors (r = 0.95, p > 0.001) and speech fluency (r = 0.92, p > 0.01). Qualitatively, the error types that increased with accentedness, were word-final consonant omission, voicing, consonant substitutions, and assimilations. In addition, all the ELL speakers showed variability in consonant coarticulation in repeating sentences, unlike the native speaker. The variability increased with accentedness up to 1/3rd of the sentences pronounced with varying inter-word errors. Discussion: The results of this pilot study align with earlier findings on the importance of consonants and coarticulation in ELL speech production. In addition, speech rate and variability in pronunciation may be connected to perceived accentedness.
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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 | 0.006 |
| 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) | 0.002 | 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".