Speech-Based Interaction in Multitask Conditions: Impact of Prompt Modality
Bibliographic record
Abstract
Speech-based interaction is often recognized as appropriate for hands-busy, eyes-busy multitask situations. The objective of this study was to explore prompt-guided speech-based interaction and the impact of prompt modality on overall performance in such situations. A dual-task paradigm was employed, with tracking as a primary task and speech-based data input as a secondary task. There were three tracking conditions: no tracking, basic, and difficult tracking. Two prompt modalities were used for the speech interaction: a dialogue with spoken prompts and a dialogue with visual prompts. Data entry duration was longer with the speech prompts than with the visual prompts, regardless of whether or not there was tracking or its level of difficulty. However, when tracking was difficult, data entry duration was similar for both spoken and visual prompts. Tracking performance was also affected by the prompt modality, with poorer performance obtained when the prompts were visual. The findings are discussed in terms of multiple resource theory and the possible implications for speech-based interactions in multitask situations. Actual or potential applications of this research include the design of speech-based dialogues for multitask situations such as driving and other hands-busy, eyes-busy situations.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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 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".