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Vineeth Johnson

dblp:404/5941 · DBLP profile ↗
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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

TopicWeightPapersLastEvidence papers
Human-robot interaction › educational robotics
social robot tutoring
0.912025
SignPepper: Multimodal Social Robot for Sign Language Teaching · HRI 2025
Accessibility and assistive technology › sign language technologies
sign language learning
0.312025
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
YearPublicationVenuePosition
2025 SignPepper: Multimodal Social Robot for Sign Language Teaching
abstract
Sign 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
HRI3