SignPepper: Multimodal Social Robot for Sign Language Teaching

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.

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Publication Details

Published
2025-03-04
DOI
https://doi.org/10.1109/hri61500.2025.10974023
Citations
1
Primary Topic
Hand Gesture Recognition Systems
Type
article
Field-Weighted Citation Impact
1.94
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article

SignPepper: Multimodal Social Robot for Sign Language Teaching

Vineeth C Johnson, Edmond Liu, Ho Seok Ahn, Bruce MacDonald et al.
1 citations
Hand Gesture Recognition Systems
1.94
article

SignPepper: Multimodal Social Robot for Sign Language Teaching

Vineeth C Johnson, Edmond Liu, Ho Seok Ahn, Bruce MacDonald, Jong Yoon Lim
article en
1 citations

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.

University of Auckland (NZ)
Quality Education
Openalex Percentile: Top 16%
Hand Gesture Recognition Systems
1.94
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