CONF Tarigopula_ICMI_2022/IDIAP Towards Accessible Sign Language Learning and Assessment Tarigopula, Neha Tornay, Sandrine Muralidhar, Skanda Magimai-Doss, Mathew hidden Markov models human skeleton estimation sign language assessment Sign language processing EXTERNAL http://publications.idiap.ch/attachments/papers/2022/Tarigopula_ICMI_2022.pdf PUBLIC ACM International Conference on Multimodal Interaction Bangalore, INDIA 2022 626-631 978-1-4503-9390-4 https://doi.org/10.1145/3536221.3556623 doi Recently, a phonology-based sign language assessment approach has been proposed using sign language production acquired in 3D space using Kinect sensor. In order to scale the sign language assessment system to realistic application, there is need to reduce the dependency on Kinect, which is not accessible to wider community, and develop solutions that can potentially work with web-cameras. This paper takes a step in that direction by investigating sign language recognition and sign language assessment in 2D space either by dropping the depth coordinate in Kinect or using methods for skeleton estimation from videos. Experimental studies on Swiss German Sign Language corpus SMILE show that, while loss of depth information leads to considerable drop in sign language recognition performance, high level of sign language assessment performance can still be obtained.