The pedagogical use of mobile speech synthesis (TTS): focus on French liaison
Bibliographic record
Abstract
We examine the impact of the pedagogical use of mobile TTS on the L2 acquisition of French liaison, a process by which a word-final consonant is pronounced at the beginning of the following word if the latter is vowel-initial (e.g. peti/t.a/mi = > peti[ta]mi ‘boyfriend’). The study compares three groups of L2 French students learning how to produce liaison over a two-month period, following a pretest-posttests design within a mixed-methods approach to data collection and analysis. Participants were divided into three groups: (1) the TTS Group used a TTS application on their mobile devices to complete weekly pronunciation tasks consisting of noticing, listen-and-categorize, and listen-and-repeat; (2) the Non-TTS Group completed the same weekly pronunciation tasks in weekly sessions with a teacher; finally, (3) the Control Group participated in weekly meetings ‘to practice their conversation skills’ with a teacher, who provided no pronunciation feedback. The results indicate that, although all three groups improved in liaison production, if considered separately (within groups), only the two experimental groups improved over time. The discussion of our findings highlights the pedagogical use of mobile TTS technology to complement and enhance the teaching of L2 pronunciation.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".