Usability Questionnaires to Evaluate Voice User Interfaces
Bibliographic record
Abstract
Voice user interfaces (VUI) have been increasingly used in everyday settings and they are growing in popularity. These interfaces have predominantly eyes-free and hands-free interactions. This kind of experiences continues to be an inceptive field compared to other input methods such as touch or using the keyboard/mouse. Thus, it is important to identify tools used to evaluate the usability of VUIs. This article presents a systematic review, in which we analyzed 57 articles and describes nine questionnaires used for evaluating the usability of VUIs, assessing the potential suitability of these questionnaires to measure different types of interactions and various usability dimensions. We found that these questionnaires were used to evaluate the usability of voice-only and voice-added VUIs: AttrakDiff, ICF-US, MOS-X, SUISQ-R, SUS, SASSI, UEQ, PARADISE and USE, where the SUS questionnaire is the most commonly used. However, its items do not directly assess voice quality, although it evaluates the general user interaction with a system. All the questionnaires include items related to three usability dimensions (effectiveness, efficiency, and satisfaction). The questionnaire with the most homogeneous coverage regarding the number of items in each aspect of usability is the SASSI questionnaire. It is a normal practice to use multiple questionnaires to obtain a more complete measurement of usability. We perceive the necessity to increase usability research about the differences between the voice interaction with diverse display types (voice-first, voice-only, voice-added) and the dialog types (command-based and conversational), and how usability affects the user expectations about the VUIs.
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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 | 0.035 | 0.096 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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 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".