Speech recognition software as an assistive device: A pilot study of user satisfaction and psychosocial impact
Bibliographic record
Abstract
The purpose of this study was to gather data concerning the psychosocial (quality of life) impact of speech recognition software on individuals with physical disabilities and to identify how satisfied these individuals were with this software as a computer access method. Two standardized questionnaires, the Psychosocial Impact of Assistive Devices Scale (PIADS) and the Quebec User Evaluation of Satisfaction with assistive Technology (QUEST) were administered to ten participants with physical disabilities who received speech recognition software following an assistive technology evaluation. The results of this study indicated that 90% of the participants were quite satisfied with speech recognition software as an assistive device and that the software had a somewhat positive psychosocial impact on their lives. Four themes emerged concerning what the participants liked most about the software: 1) the software provided a method of access when they were not previously accessing a computer, 2) the software increased independence, 3) the software made computer use more efficient, and 4) the software provided a choice or flexibility in computer access. Although this study demonstrated that these speech recognition software users are generally satisfied with the software and it has had a positive impact on their life, it also suggests that there is a need to examine the role of training on satisfaction and successful use of the software.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".