Effectiveness of using discrete utterance speech recognition software
Bibliographic record
Abstract
This descriptive study explored text generation speeds, recognition accuracy, and participants' perceptions of the advantages/disadvantages of using discrete utterance speech recognition software. Six participants (ages 19 to 35) with physical disabilities and intelligible speech were interviewed about their experiences using their speech recognition software. Using this software on their home computers, the participants completed five dictation tasks. Average individual dictation speeds ranged from 9 to 15 words per minute and average recognition accuracy ranged from 62 to 84%. The use of formatting and correction commands resulted in an average of two utterances being required to generate each dictation word. Participants found that recognition accuracy was not acceptable and that their speech recognition software was appropriate for use with word processors but had limited use with other applications. This study found that discrete utterance speech recognition can be effective for people who cannot use a keyboard to write. However, the slow speeds of text generation achieved by the participants suggest that people who can use a keyboard to some extent (e.g., slow typists) may not be able to increase their speed by using discrete utterance speech recognition software. The advantages and disadvantages of discrete products that are also relevant to continuous products are discussed.
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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 | 0.004 | 0.040 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".