Intelligent personal assistants: can they understand and be understood by accented L2 learners?
Bibliographic record
Abstract
Second/foreign language (L2) classrooms do not always provide opportunities for input and output practice [Lightbown, P. M. (2000). Classroom SLA research and second language teaching. Applied Linguistics, 21(4), 431–462]. The use of smart speakers such as Amazon Echo and its associated voice-controlled intelligent personal assistant (IPA) Alexa can help address this limitation because of its ability to extend the reach of the classroom, motivate practice, and encourage self-learning. Our previous study on the pedagogical use of Echo revealed that its use gave L2 learners ample opportunities for stress-free input exposure and output practice [Moussalli, S., & Cardoso, W. (2016). Are commercial ‘personal robots’ ready for language learning? Focus on second language speech. In S. Papadima-Sophocleous, L. Bradley, & S. Thouësny (Eds.), CALL communities and culture – short papers from EUROCALL 2016 (pp. 325–329). However, the results also suggested that beginner learners, depending on their levels of accentedness, experienced difficulties interacting with and being understood by Echo. Interestingly, this observation differs from findings involving human-to-human interactions, which suggest that a speaker’s foreign accent does not impede intelligibility. In this article, we report the results of a study that investigated Echo’s ability to recognize and process non-native accented speech at different levels of accentedness, based on the accuracy of its replies for a set of pre-established questions. Using a variety of analytical methods (i.e. judges’ ratings of learners’ pronunciation, learners’ ratings of Echo’s pronunciation, transcriptions of Echo’s interactions, surveys and interviews) and via a multidimensional analysis of the data collected, our results indicate that L2 learners have no problems understanding Echo and that it adapts well to their accented speech (Echo is comparable to humans in terms of comprehensibility and intelligibility). Our results also show that L2 learners use a variety of strategies to mitigate the communication breakdown they experienced with Echo.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.003 |
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".