Comparing the Effectiveness of Speech and Physiological Features in Explaining Emotional Responses during Voice User Interface Interactions
Bibliographic record
Abstract
The rapid rise of voice user interface technology has changed the way users traditionally interact with interfaces, as tasks requiring gestural or visual attention are swapped by vocal commands. This shift has equally affected designers, required to disregard common digital interface guidelines in order to adapt to non-visual user interaction (No-UI) methods. The guidelines regarding voice user interface evaluation are far from the maturity of those surrounding digital interface evaluation, resulting in a lack of consensus and clarity. Thus, we sought to contribute to the emerging literature regarding voice user interface evaluation and, consequently, assist user experience professionals in their quest to create optimal vocal experiences. To do so, we compared the effectiveness of physiological features (e.g., phasic electrodermal activity amplitude) and speech features (e.g., spectral slope amplitude) to predict the intensity of users’ emotional responses during voice user interface interactions. We performed a within-subjects experiment in which the speech, facial expression, and electrodermal activity responses of 16 participants were recorded during voice user interface interactions that were purposely designed to elicit frustration and shock, resulting in 188 analyzed interactions. Our results suggest that the physiological measure of facial expression and its extracted feature, automatic facial expression-based valence, is most informative of emotional events lived through voice user interface interactions. By comparing the unique effectiveness of each feature, theoretical and practical contributions may be noted, as the results contribute to voice user interface literature while providing key insights favoring efficient voice user interface evaluation.
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.
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 | 0.002 | 0.012 |
| 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 | 0.000 | 0.000 |
| Research integrity | 0.001 | 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 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".