TY - GEN
T1 - A User-Centered Evaluation of the Data-Driven Sign Language Avatar System
T2 - 10th Conference on Human-Agent Interaction, HAI 2022
AU - Imashev, Alfarabi
AU - Oralbayeva, Nurziya
AU - Kimmelman, Vadim
AU - Sandygulova, Anara
N1 - Funding Information:
This work was supported by the Nazarbayev University Faculty Development Competitive Research Grant Program 2022-2024 “Kazakh-Russian Sign Language Processing: Data, Tools, and Interaction”. Award number is 11022021FD2902.
Publisher Copyright:
© 2022 ACM.
PY - 2022/12/5
Y1 - 2022/12/5
N2 - Sign Languages (SL) are a form of communication in the visual-gestural modality, and are full-fledged natural languages. Recent years have witnessed the increase in the use of virtual avatars as virtual assistants. Research into sign language recognition has demonstrated promising potential for robust automatic sign language recognition. However, the area of sign language synthesis is still in its infancy. This explains the underdevelopment of virtual intelligent signing systems. Additionally, existing models are often restricted to manually written rules and require expert knowledge, while data-driven approach could provide a better solution. Apart from the development of signing systems, research indicates a gap in the evaluation thereof by sign language users. In this paper, we propose a data-driven sign language interpreting avatar and its subjective evaluation. We present findings from a pilot study with the deaf evaluating two different avatars against a human sign language interpreter using the metrics that are believed to bring out important insights and narratives for the users in terms of their perceptions of the avatars.
AB - Sign Languages (SL) are a form of communication in the visual-gestural modality, and are full-fledged natural languages. Recent years have witnessed the increase in the use of virtual avatars as virtual assistants. Research into sign language recognition has demonstrated promising potential for robust automatic sign language recognition. However, the area of sign language synthesis is still in its infancy. This explains the underdevelopment of virtual intelligent signing systems. Additionally, existing models are often restricted to manually written rules and require expert knowledge, while data-driven approach could provide a better solution. Apart from the development of signing systems, research indicates a gap in the evaluation thereof by sign language users. In this paper, we propose a data-driven sign language interpreting avatar and its subjective evaluation. We present findings from a pilot study with the deaf evaluating two different avatars against a human sign language interpreter using the metrics that are believed to bring out important insights and narratives for the users in terms of their perceptions of the avatars.
KW - avatars
KW - data-driven
KW - HCI
KW - sign language
KW - subjective evaluation
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U2 - 10.1145/3527188.3561923
DO - 10.1145/3527188.3561923
M3 - Conference contribution
AN - SCOPUS:85144612850
T3 - HAI 2022 - Proceedings of the 10th Conference on Human-Agent Interaction
SP - 194
EP - 202
BT - HAI 2022 - Proceedings of the 10th Conference on Human-Agent Interaction
PB - Association for Computing Machinery, Inc
Y2 - 5 December 2022 through 8 December 2022
ER -