GSI DEMO: Multiuser Gesture / Speech Interaction over Digital Tables by Wrapping Single User Applications
Bibliographic record
Abstract
Most commercial software applications are designed for a single user using a keyboard/mouse over an upright monitor. Our interest is exploiting these systems so they work over a digital table. Mirroring what people do when working over traditional tables, we want multiple people to interact with the tabletop application and with each other via rich speech and hand gestures. In previous papers, we illustrated multi-user gesture and speech interaction on a digital table for geospatial applications Google Earth, Warcraft III and The Sims. In this paper, we describe our underlying architecture: GSI DEMO. First, GSI DEMO creates a run-time wrapper around existing single user applications: it accepts and translates speech and gestures from multiple people into a single stream of keyboard and mouse inputs recognized by the application. Second, it lets people use multimodal demonstration instead of programming to quickly map their own speech and gestures to these keyboard/mouse inputs. For example, continuous gestures are trained by saying Computer, when I do [one finger gesture], you do [mouse drag] . Similarly, discrete speech commands can be trained by saying Computer, when I say [layer bars], you do [keyboard and mouse macro] . The end result is that end users can rapidly transform single user commercial applications into a multi-user, multimodal digital tabletop system.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.039 | 0.009 |
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".