Publications

Abstract : The development of Neural Machine Translation (NMT) systems has attained prominent position in language translation tasks. However, it faces huge challenges in translating the new words and out-of-vocabularies. This problem is identified as a major drawback of conventional NMT systems in language translation results more copied outputs. In addition to that, it places the risks in understanding multilingual language structures and word relationships. In this paper, we propose novel deep stacked GRU algorithm based NMT System to address the aforementioned challenges and handles multilingual sentences based translation tasks efficiently. We aimed to develop the proposed model for translating the spoken sentences into sign words. The generated sign words (glosses) are mapped with sign gesture images to automate the sign gesture video generation process using deep generative models. The proposed hybrid NMT model has been evaluated qualitatively and quantitatively using different benchmark sign language datasets. The improved BLEU Score shows the outperformance of our model compared with earlier approaches. We also evaluated the proposed model using our self created Indian sign language corpus (ISL-CSLTR). The final result shows the achievement of greater translation results with minimal processing cost.