Sign language is a basic form of communication for hearing-impaired individuals. An evaluation of the quality of sign language gestures helps improve the efficiency of sign language learning. This article proposes SignEvaluator, a sign language quality assessment system with a movement quality feature extractor and assessment generator. In the former, three quality measures are proposed for gestures and sentences. The trajectory of the palm is mapped onto position space with kernel density estimation. For finger movements, the instantaneous energy and curvature of the gesture signals are extracted with Bézier curves. Meanwhile, the performer's familiarity with gestures is indicated by the movement fluency metric of sentences. In the assessment generator, the final assessment results are calculated by combining the weights of different quality metrics and the confidence of different gesture levels. The results indicate that SignEvaluator obtained an F1-score of 0.89 for 702 sentences collected from 20 performers.

Sign language is widely used among deaf. Many existing studies on Sign Language Recognition (SLR) focus on addressing communication barriers between deaf and hearing people. However, current studies face two challenges: the recognition result is highly dependent on the wearable device placements, and the existence of sentences in the training set. To address the challenges, this paper proposed EasyHear – a Transformer-based Chinese SLR system with a generalization approach. For generalization of wearable device placements, an anchor-based signal rotation correction algorithm is proposed to eliminate the impact of variations in wearing positions. In terms of generalization of sentences, a gesture code is constructed to reflect the closeness of gestures after defining an intimate entropy of gestures. Moreover, a semantic code is developed by training a neural network on a large corpus to reflect the intimacy of Chinese Sign Language (CSL) words on unseen sentences in the training set. A Transformer-based model combined with the rotated gesture signals as well as gesture and semantic codes is suggested to improve recognition performance across various wearing positions and sentences. The results show that EasyHear achieved an average word error rate of 21.60% for samples of 712 commonly used CSL sentences.