Assessing the Pedagogical Potential of Google Translate's Speech Capabilities: Focus on French Pronunciation
Why this work is in the frame
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Bibliographic record
Abstract
As the capabilities of web-based machine translation develop, online translators such as Google Translate (GT) have attracted computer-assisted language learning (CALL) researchers’ attention for their potential to aid second/foreign language (L2) instruction. Using its built-in text-to-speech (TTS) and automatic speech recognition (ASR) features, GT can be used for L2 pronunciation practice. The aim of this study (part of a larger project investigating L2 learners’ use of speech technologies in homework settings) is to examine the impact of self-regulated pronunciation practice using GT's TTS and ASR features on the development of French liaison (the re-syllabification of latent consonants when they appear in consonant-plus-vowel contexts across words, e.g., /z/ in tes amis [te.za.mi] “your friends”). Participants were 20 adult beginner learners of French studying at an English-speaking university in Canada. Their phonological development (i.e., awareness, perception, and production) was assessed before (pretest) and after (immediate and delayed posttests) the completion of a semi-autonomous, GT-based pronunciation practice. The results of the analysis of variance (ANOVA, the statistical method used) indicate that the proposed treatment led to a statistically significant improvement in liaison production between the pretest and the delayed posttest, while phonological awareness and perception remained unaffected, probably due to a ceiling effect.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.027 | 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 it