Abstract
In this paper, we developed HearUsNow, a deep learning-based system designed to assist deaf teenagers in correcting speech order and learning sign language, thereby improving their communication skills. This interactive system incorporates a deep learning-based back-end with a user-friendly front-end interface, aiming to bridge the gap between these individuals and their real-world communication needs. In this system, deaf teenagers receive corrections on sentence structure and accompanying sign language instructional images after entering sentences, and sign language practice is facilitated through a camera interface that provides real-time feedback. The results of a pilot study (nā=ā6) showed high usability, interaction fluency, and accuracy of correction and recognition of the interactive system. Over a seven-day period, the participants mastered more than 30 gesture combinations for everyday communication, and parents noted improved self-confidence and parent-child relationships.
W. Guo and J. BaiāCo-first authors of this poster.
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References
Aran, O., et al.: Signtutor: an interactive system for sign language tutoring. IEEE Multimedia 16, 81ā93 (2009)
Hein, Z., Htoo, T.P., Aye, B., Htet, S.M., Ye, K.Z.: Leap motion based myanmar sign language recognition using machine learning. In: 2021 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (ElConRus), pp. 2304ā2310. IEEE (2021)
Rastogi, R., Mittal, S., Agarwal, S.: A novel approach for communication among blind, deaf and dumb people. In: 2015 2nd International Conference on Computing for Sustainable Global Development (INDIACom), pp. 605ā610. IEEE (2015)
Marschark, M., Spencer, P.E.: The Oxford Handbook of Deaf Studies, Language, and Education, vol. 2. Oxford University Press, Oxford (2010)
Sultan, A., Makram, W., Kayed, M., Ali, A.A.: Sign language identification and recognition: a comparative study. Open Comput. Sci. 12, 191ā210 (2022)
Devlin, J., Chang, M.-W., Lee, K., Toutanova, K.: Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805. (2018)
Cui, Y., Che, W., Liu, T., Qin, B., Wang, S., Hu, G.: Revisiting pre-trained models for Chinese natural language processing. arXiv preprint arXiv:2004.13922. (2020)
Rastgoo, R., Kiani, K., Escalera, S.: Sign language recognition: a deep survey. Expert Syst. Appl. 164, 113794 (2021)
Guo, W., Li, S., Zhang, Z., Chen, Z., Chang, K.H., Wang, S.: A āmagic worldā for children: design and development of a serious game to improve spatial ability. Comput. Anim. Virtual Worlds 34(3), e2181 (2023)
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Guo, W., Bai, J., Li, H., Chang, K.H., Xu, J. (2024). Design and Development of a Deep Learning-Based Sign Language Learning Aid for Deaf Teenagers. In: Stephanidis, C., Antona, M., Ntoa, S., Salvendy, G. (eds) HCI International 2023 ā Late Breaking Posters. HCII 2023. Communications in Computer and Information Science, vol 1957. Springer, Cham. https://doi.org/10.1007/978-3-031-49212-9_20
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DOI: https://doi.org/10.1007/978-3-031-49212-9_20
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