We need to talk
Bibliographic record
Abstract
Speech and natural language remain our most natural form of interaction; yet the HCI community have been very timid about focusing their attention on designing and developing spoken language interaction techniques. This may be due to a widespread perception that perfect domain-independent speech recognition is an unattainable goal. Progress is continuously being made in the engineering and science of speech and natural language processing, however, and there is also recent research that suggests that many applications of speech require far less than 100% accuracy to be useful in many contexts. Engaging the CHI community now is timely -- many recent commercial applications, especially in the mobile space, are already tapping the increased interest in and need for natural user interfaces (NUIs) by enabling speech interaction in their products. As such, the goal of this panel is to bring together interaction designers, usability researchers, and general HCI practitioners to discuss the opportunities and directions to take in designing more natural interactions based on spoken language, and to look at how we can leverage recent advances in speech processing in order to gain widespread acceptance of speech and natural language interaction.
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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.010 | 0.010 |
| Scholarly communication | 0.013 | 0.021 |
| Open science | 0.002 | 0.009 |
| Research integrity | 0.008 | 0.017 |
| Insufficient payload (model declined to judge) | 0.135 | 0.122 |
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".