“Voice-First Interfaces in a GUI-First Design World”: Barriers and Opportunities to Supporting VUI Designers On-the-Job
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Voice user interfaces (VUIs) are currently experiencing rapid growth as commercial devices like Google Home, Amazon Echo, and Apple Homepod are adopted by users. However, due to the pace of this growth, the tech industry has had to adapt quickly and vigorously to keep up with demand. Due to this, we currently have limited understanding of the environment of VUI design in industry, including the various multitude of practices and tools that are used. We also have a limited understanding of the barriers VUI designers currently still face. To address such knowledge gaps, we conducted a large-scale online survey to explore the design practices employed by VUI industry designers on-the-job, and the barriers and needs of VUI designers. We found that despite the availability of a wide range of guidelines, textbooks, tools, etc, there are significant gaps in the adoption of these tools within VUI industry design, and that designers rely on their previous experience in developing GUIs when designing VUIs. Based on our survey findings, we provide recommendations for how the HCI community may direct research efforts in developing tools to assist designers in overcoming existing barriers and build usable and adoptable VUIs.
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.
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 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it