Designing Speech and Multimodal Interactions for Mobile, Wearable, and Pervasive Applications
Bibliographic record
Abstract
Traditional interfaces are continuously being replaced by mobile, wearable, or pervasive interfaces. Yet when it comes to the input and output modalities enabling our interactions, we have yet to fully embrace some of the most natural forms of communication and information processing that humans possess: speech, language, gestures, thoughts. Very little HCI attention has been dedicated to designing and developing spoken language and multimodal interaction techniques, especially for mobile and wearable devices. In addition to the enormous, recent engineering progress in processing such modalities, there is now sufficient evidence that many real-life applications do not require 100% accuracy of processing multimodal input to be useful, particularly if such modalities complement each other. This multidisciplinary, two-day workshop will bring together interaction designers, usability researchers, and general HCI practitioners to analyze the opportunities and directions to take in designing more natural interactions with mobile and wearable devices, and to look at how we can leverage recent advances in speech and multimodal processing.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".