The effect of automatic speech recognition systems on speaking workload and task efficiency
Bibliographic record
Abstract
PURPOSE: This investigation explored the speech-production behaviors associated with the use of automatic speech recognition (ASR) software for dictation of spontaneous and scripted material by individuals with and without spinal cord injury (SCI). The variables of interest in determining speaking workload and efficiency included syllables per breath group, frequency of breath groups, frequency of apnea, time needed for dictation and number of words spoken during a dictation task. METHOD: Twelve individuals participated, six with SCI and six able-bodied cohorts matched for age, sex and height. Subjects dictated with continuous-speech ASR, discrete-word ASR and no ASR in a spontaneous-speaking situation, as well as in a scripted speaking situation. RESULTS: For all variables, differences amongst dictation conditions were significant. No significant differences were found between speaker groups. Dictation with both discrete-word and continuous-speech ASR resulted in a decrease in the number of syllables produced per breath group, increases in the frequency of breath groups and apnea, with differences from normal being greater for dictation with discrete-word ASR. In addition, when participants dictated with either type of ASR, the amount of time and number of words produced were significantly greater than that associated with production of the same message without ASR, requiring 'more work' on the part of the speaker and ultimately reducing the efficiency with which a message was produced. CONCLUSIONS: From a human factors perspective, these results suggest that ASR software, especially discrete-word ASR, has the potential to increase energy expenditure during dictation over a prolonged period of time, thereby increasing speech workloads and the potential for overuse of the laryngeal system.
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.
How this classification was reachedexpand
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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".