Mobilizing Instruction in a Second-Language Context: Learners’ Perceptions of Two Speech Technologies
Bibliographic record
Abstract
We report the results of two empirical studies that investigated the use of mobile text-to-speech synthesizers (TTS) and automatic speech recognition (ASR) as tools to promote the development of pronunciation skills in L2 French. Specifically, the study examined learners’ perceptions of the pedagogical use of these tools in learning a French segment (the vowel /y/, as in tu ‘you’) and a suprasegmental feature (across-word resyllabification/liaison, observed in petit enfant ‘small child’), in a mobile-assisted context. Our results indicate that, when used in a “learn anytime anywhere” mobile setting, the participants believe that they have: (1) increased and enhanced access to input; and (2) multiple opportunities for speech output and (3) for the development of prediction skills. Interestingly, these findings meet the requirements for successful L2 learning, one that recommends the inclusion of pedagogical activities that promote exposure to input (Nation & Newton 2009), multiple opportunities for output (Swain 1995), and the development of prediction skills (Dickerson 2015) to foster learner autonomy and, consequently, to maximize classroom time by extending the reach of the classroom. Our findings also indicate that participants recognize the pedagogical importance of TTS and ASR, and enjoy the mobile-enhanced learning environment afforded by these two technologies.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".