Demo Data from SIGIR 2026 / reviewers in the wild / expert
Vineeth Johnson
dblp:404/5941
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2025
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1 · 1 since 2021Human-computer interaction and ubiquitous computing · 1 · 1 since 2021
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Human-computer interaction and pervasive computing
1 paper |
Learning and educational technologies · 44% Human-robot interaction · 44% Accessibility and assistive technology · 13% |
Topics — the 2 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Human-robot interaction › educational robotics
social robot tutoring |
0.9 | 1 | 2025 | SignPepper: Multimodal Social Robot for Sign Language Teaching · HRI 2025 |
Accessibility and assistive technology › sign language technologies
sign language learning |
0.3 | 1 | 2025 | SignPepper: Multimodal Social Robot for Sign Language Teaching · HRI 2025 |
Methods — techniques the papers use, named apart from their topics
speech recognition · 0.9sign language recognition · 0.9large language model · 0.93d convolutional neural network · 0.9
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2025 | SignPepper: Multimodal Social Robot for Sign Language TeachingabstractSign language is an essential communication tool, however, it can be highly challenging for non-deaf students to learn. We propose a Pepper robot based sign language teaching assistant called SignPepper, with the ability to communicate in both spoken and sign language. Using Whisper speech to text and Llama 3.3, SignPepper can engage in two way spoken lessons, with the ability to physically demonstrate signs to students. Furthermore, using 3D convolutional neural networks trained on sign language recognition, SignPepper can watch, analyze and give personalized feedback on students attempts at performing newly learned signs in real-time; including hand-based error localization. Edmond Liu, Jong Yoon Lim, Vineeth Johnson, Bruce A. MacDonald, Ho Seok Ahn |
HRI | 3 |