Voice Assistant as a Modern Contrivance to Acquire Oral Fluency: An Acoustical and Computational Analysis
Bibliographic record
Abstract
A number of methods have been experimented to improve fluency and accuracy of second language learners in English. In acquisition of fluency in oral communication, the second language learners have many obstacles such as lack of exposure and lack of opportunities. Focus on the skills targeted and individualization of attention are needed to accomplish the objectives. The objective of this article is to enable second language learners to gain fluency in English through the use of Voice Assistant in L2 classrooms. A number of Voice Assistants were experimented to find out the suitable application for the study. The study was carried out in a university in India to find out the effectiveness of Voice Assistant for enhancing fluency. The hypothesis was tested with forty students. Pre-test and post-test were conducted to find out the fluency level of the Experimental and the Control Groups through audio analysis. The data was analyzed to indicate the significance of the method. The progress of the learners experimented with Voice Assistant was most encouraging, and they found this method quite appropriate and easy too in acquisition of oral fluency.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".