Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition

• URL: http://arxiv.org/abs/2204.08747v1
• Date: Tue, 19 Apr 2022 08:43:03 GMT
• Title: Multi-View Spatial-Temporal Network for Continuous Sign Language Recognition
• Authors: Ronghui Li and Lu Meng
• Abstract summary: This paper proposes a multi-view spatial-temporal continuous sign language recognition network. It is tested on two public sign language datasets SLR-100 and PHOENIX-Weather 2014T (RWTH)
• Score: 0.76146285961466
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Sign language is a beautiful visual language and is also the primary language used by speaking and hearing-impaired people. However, sign language has many complex expressions, which are difficult for the public to understand and master. Sign language recognition algorithms will significantly facilitate communication between hearing-impaired people and normal people. Traditional continuous sign language recognition often uses a sequence learning method based on Convolutional Neural Network (CNN) and Long Short-Term Memory Network (LSTM). These methods can only learn spatial and temporal features separately, which cannot learn the complex spatial-temporal features of sign language. LSTM is also difficult to learn long-term dependencies. To alleviate these problems, this paper proposes a multi-view spatial-temporal continuous sign language recognition network. The network consists of three parts. The first part is a Multi-View Spatial-Temporal Feature Extractor Network (MSTN), which can directly extract the spatial-temporal features of RGB and skeleton data; the second is a sign language encoder network based on Transformer, which can learn long-term dependencies; the third is a Connectionist Temporal Classification (CTC) decoder network, which is used to predict the whole meaning of the continuous sign language. Our algorithm is tested on two public sign language datasets SLR-100 and PHOENIX-Weather 2014T (RWTH). As a result, our method achieves excellent performance on both datasets. The word error rate on the SLR-100 dataset is 1.9%, and the word error rate on the RWTHPHOENIX-Weather dataset is 22.8%.

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