Assessing the Pedagogical Potential of Google Translate's Speech Capabilities: Focus on French Pronunciation
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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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) | 0.000 | 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 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".