WearSign

Abstract

Sign language translation (SLT) is considered as the core technology to break the communication barrier between the deaf and hearing people. However, most studies only focus on recognizing the sequence of sign gestures (sign language recognition (SLR)), ignoring the significant difference of linguistic structures between sign language and spoken language. In this paper, we approach SLT as a spatio-temporal machine translation task and propose a wearable-based system, WearSign, to enable direct translation from the sign-induced sensory signals into spoken texts. WearSign leverages a smartwatch and an armband of ElectroMyoGraphy (EMG) sensors to capture the sophisticated sign gestures. In the design of the translation network, considering the significant modality and linguistic gap between sensory signals and spoken language, we design a multi-task encoder-decoder framework which uses sign glosses (sign gesture labels) for intermediate supervision to guide the end-to-end training. In addition, due to the lack of sufficient training data, the performance of prior studies usually degrades drastically when it comes to sentences with complex structures or unseen in the training set. To tackle this, we borrow the idea of back-translation and leverage the much more available spoken language data to synthesize the paired sign language data. We include the synthetic pairs into the training process, which enables the network to learn better sequence-to-sequence mapping as well as generate more fluent spoken language sentences. We construct an American sign language (ASL) dataset consisting of 250 commonly used sentences gathered from 15 volunteers. WearSign achieves 4.7% and 8.6% word error rate (WER) in user-independent tests and unseen sentence tests respectively. We also implement a real-time version of WearSign which runs fully on the smartphone with a low latency and energy overhead.