Conversational Voice Interfaces: Translating Research Into Actionable Design
Bibliographic record
Abstract
HCI research has for long been dedicated to better and more naturally facilitating information transfer between humans and machines.Unfortunately, humans' most natural form of communication, speech, is also one of the most difficult modalities to be understood by machines -despite, and perhaps, because it is the highest-bandwidth communication channel we possess.As significant research efforts in engineering have been spent on improving machines' ability to understand speech, research is only beginning to make the same improvements in understanding how to appropriately design these speech interfaces to be user-friendly and adoptable.Issues such as variations in error rates when processing speech, and difficulties in learnability and explainability (to name a few), are often in contrast with claims of success from industry.Along with this, designers themselves are making the transition to designing for speech and voice-enabled interfaces.Recent research has demonstrated the struggle for designers to translate their current experiences in graphical user interface design into speech interface design.Research has also noted the lack of any user-centered design principles or consideration for usability or usefulness in the same ways as graphical user interfaces have benefited from heuristic design guidelines.The goal of this course is to inform the CHI community of the current state of speech and natural language research, to dispel some of the myths surrounding speech-based interaction, as well as to inform participants about currently existing design tools, methods and resources for speech interfaces (and provide hands-on experience with working with them).Through this, we hope that HCI researchers and practitioners will learn how to combine recent advances in speech processing with user-centred principles in designing more usable and useful speech-based interactive systems.
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How this classification was reachedexpand
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 | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".