Bibliographic record
Abstract
This study evaluated the voice of a modern English text-to-speech (TTS) system in an English as a foreign language (EFL) context in terms of its speech quality, ability to be understood by L2 users, and potential for focus on specific language forms. Twenty-nine Brazilian EFL learners listened to stories and sentences, produced by a TTS voice and a human voice, and rated them on a 6-point Likert scale according to holistic criteria for evaluating pronunciation: Comprehensibility, naturalness, and accuracy. In addition, they were asked to answer a set of comprehension questions (to assess understanding), to complete a dictation/transcription task to measure intelligibility, and to identify whether the target past -ed form was present or not in decontextualized sentences. Results indicate that the performance of both the TTS and human voices were perceived similarly in terms of comprehensibility, while ratings for naturalness were unfavorable for the synthesized voice. For text comprehension, dictation, and aural identification tasks, participants performed relatively similarly in response to both voices. These findings suggest that TTS systems have the potential to be used as pedagogical tools for L2 learning, particularly in EFL settings, where natural occurrence of the target language is limited or non-existent.
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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 | 0.001 |
| 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".