Improving Sign Language Processing Via Few-Shot Machine Learning

Improving the efficiency of communication of deaf and hard of hearing people by processing sign language using artificial intelligence is an important task both socially and technologically. One of the ways to solve this problem is a fairly cheap and accessible marker method. The method is based on the registration of electromyographic (EMG) muscle signals using bracelets worn on the arm. To improve the quality of recognition of gestures recorded by the marker method, a modification of the marker method is proposed — duplication of EMG sensors in combination with a lowframe machine learning approach. We experimentally study the possibilities of improving the quality of processing of sign language by duplicating EMG sensors as well as by reducing the volume of the dataset required for training machine learning tools. In the latter case, we compare several technologies of the few-shot approach. Our experiments show that training with few-shot neural nets on 56k samples we can achieve better results than training on random forest with 160k samples. The use of a minimum number of sensors in combination with few-shot signal processing techniques provides the possibility of organizing quick and cost-effective interaction with people with hearing and speech disabilities.