Speech Interaction to Control a Hands-Free Delivery Robot for High-Risk Health Care Scenarios
Bibliographic record
Abstract
The Covid-19 pandemic has had a widespread effect across the globe. The major effect on health-care workers and the vulnerable populations they serve has been of particular concern. Near-complete lockdown has been a common strategy to reduce the spread of the pandemic in environments such as live-in care facilities. Robotics is a promising area of research that can assist in reducing the spread of covid-19, while also preventing the need for complete physical isolation. The research presented in this paper demonstrates a speech-controlled, self-sanitizing robot that enables the delivery of items from a visitor to a resident of a care facility. The system is automated to reduce the burden on facility staff, and it is controlled entirely through hands-free audio interaction in order to reduce transmission of the virus. We demonstrate an end-to-end delivery test, and an in-depth evaluation of the speech interface. We also recorded a speech dataset with two conditions: the talker wearing a face mask and the talker not wearing a face mask. We then used this dataset to evaluate the speech recognition system. This enabled us to test the effect of face masks on speech recognition interfaces in the context of autonomous systems.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 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 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".