AI Enabled Tutor for Accessible Training

Banerjee, Ayan; Lamrani, Imane; Hossain, Sameena; Paudyal, Pradip; Gupta, Sandeep

doi:10.1007/978-3-030-52237-7_3

Cited by 4 publications

(1 citation statement)

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“…Individually different components of the CSignGen framework were evaluated in related research. The gesture learning mechanism was evaluated in [2], and automated feedback based on sublexical properties was evaluated in [10]. A different approach to finding semantic similarity was investigated in [9].…”

Section: Use Of Edgcon In Dhh Educationmentioning

confidence: 99%

EdGCon: Auto-assigner of Iconicity Ratings Grounded by Lexical Properties to Aid in Generation of Technical Gestures

Hossain

Kamboj

Maity

et al. 2023

Proceedings of the 38th ACM/SIGAPP Symposium on Applied Computing

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Gestures that share similarities in their forms and are related in their meanings, should be easier for learners to recognize and incorporate into their existing lexicon. In that regard, to be more readily accepted as standard by the Deaf and Hard of Hearing community, technical gestures in American Sign Language (ASL) will optimally share similar in forms with their lexical neighbors. We utilize a lexical database of ASL, ASL-LEX, to identify lexical relations within a set of technical gestures. We use automated identification for 3 unique sub-lexical properties in ASL-location, handshape and movement. 𝐸𝑑𝐺𝐶𝑜𝑛 assigned an iconicity rating based on the lexical property similarities of the new gesture with an existing set of technical gestures and the relatedness of the meaning of the new technical word to that of the existing set of technical words. We collected 30 ad hoc crowdsourced technical gestures from different internet websites and tested them against 31 gestures from the DeafTEC technical corpus. We found that 𝐸𝑑𝐺𝐶𝑜𝑛 was able to correctly auto-assign the iconicity ratings 80.76% of the time. 1

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Section: Use Of Edgcon In Dhh Educationmentioning

confidence: 99%