Exploring users’ perceptions of ASR for writing narrative texts
Bibliographic record
Abstract
This study examines how users perceive Automatic Speech Recognition (ASR) as a tool for writing narrative texts, and compares the perceptions of two groups of users: native and non-native English writers. As such, this study aimes to answer the following questions: (1) How do English writers perceive the use of ASR as a writing tool?; and (2) How do native and non-native English writers’ perceptions compare in terms of using ASR as a writing tool? To answer these questions, we employed the Technology Acceptance Model 2 (TAM2) to investigate 60 participants’ perceptions of utilizing ASR for producing narrative texts. Our findings from analyzing seven components of TAM2 show that writers express a positive attitude towards utilizing ASR as a tool for composing texts. Our findings also indicate no noticeable differences between how native and non-native English writers perceive the usefulness of ASR for creating texts. This is contrary to our hypothesis that native speakers, owing to their more advanced pronunciation skills in English, might have a more favorable attitude towards using ASR.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".