Effectiveness of using discrete utterance speech recognition software
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it