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. While significant research efforts, from engineering, to linguistic, and to cognitive sciences, have been spent on improving machines' ability to understand speech, the CHI community (and the HCI field at large) has been relatively timid in embracing this modality as a central focus of research. This can be attributed in part to the relatively discouraging levels of accuracy in understanding speech, in contrast with often-unfounded claims of success from industry, but also to the intrinsic difficulty of designing and especially evaluating speech and natural language interfaces. As such, the development of interactive speech-based systems is mostly driven by engineering efforts to improve such systems with respect to largely arbitrary performance metrics. Such developments have often been void of any user-centered design principles or consideration for usability or usefulness. 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 provide an opportunity for researchers and practitioners to learn more about how speech recognition and speech synthesis work, what are their limitations, and how they could be used to enhance current interaction paradigms. 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.
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 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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.002 |
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".