Body Language for VUIs: Exploring Gestures to Enhance Interactions with Voice User Interfaces
Why this work is in the frame
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Bibliographic record
Abstract
With the progress in Large Language Models (LLMs) and rapid development of wearable smart devices like smart glasses, there is a growing opportunity for users to interact with on-device virtual assistants through voice and gestures with ease. Although voice user interfaces (VUIs) have been widely studied, the potential uses of full-body gestures in VUIs that can fully understand users’ surroundings and gestures are relatively unexplored. In this two-phase research using a Wizard-of-Oz approach, we aim to investigate the role of gestures in VUI interactions and explore their design space. In an initial exploratory user study with six participants, we identify influential factors for VUI gestures and establish an initial design space. In the second phase, we conducted a user study with 12 participants to validate and refine our initial findings. Our results showed that users are open and ready to adopt and utilize gestures to interact with multi-modal VUIs, especially in scenarios with poor voice capture quality. The study also highlighted three key categories of gesture functions for enhancing multi-modal VUI interactions: context reference, alternative input, and flow control. Finally, we present a design space for multi-modal VUI gestures along with demonstrations to enlighten future design for coupling multi-modal VUIs with gestures.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.001 |
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