An Investigation of the Effectiveness of Online Text-to-Speech Tools in Improving EFL Teacher Trainees’ Pronunciation
Bibliographic record
Abstract
Given the limited time for instruction in the classroom, pronunciation often ends up as the most neglected aspect of language teaching. However, in cases when the learner’s pronunciation is expected to be good or native-like, as is expected of language teacher trainees, out-of-class self-study options become prominent. This study aimed to investigate the effectiveness of online text-to-speech tools used by EFL teacher trainees when preparing for an oral achievement test. The study was conducted with 43 junior year teacher trainees at a large state university in Turkey. A pre- and post-test experimental design was used. Both qualitative and quantitative data were collected through a questionnaire to explore the trainees’ opinions related to pronunciation and their practices to improve this, a post reflection questionnaire for the effectiveness of the procedure, and a speaking rubric to evaluate the oral presentations of the trainees. The results indicate that the trainees perceived a native-like accent as a measure of being a good language teacher. It was also revealed that text-to-speech websites are effective self-study tools in improving trainees’ pronunciation.
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 | 0.004 | 0.012 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".