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Advancements in Deep Learning for Automated Dubbing in Indian Languages
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Author(s): Sasithradevi A. (Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, India), Shoba S. (Centre for Advanced Data Science, Vellore Institute of Technology, Chennai, India), Manikandan E. (Centre for Innovation and Product Development, Vellore Institute of Technology, Chennai, India)and Chanthini Baskar (Vellore Institute of Technology, Chennai, India)
Copyright: 2023
Pages: 10
Source title:
Deep Learning Research Applications for Natural Language Processing
Source Author(s)/Editor(s): L. Ashok Kumar (PSG College of Technology, India), Dhanaraj Karthika Renuka (PSG College of Technology, India)and S. Geetha (Vellore Institute of Technology, India)
DOI: 10.4018/978-1-6684-6001-6.ch009
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Abstract
After the proliferation of deep learning technologies in computer vision applications, natural language processing has used deep learning methods for its building steps like segmentation, classification, prediction, understanding, and recognition. Among different natural language processing domains, dubbing is one of the challenging tasks. Deep learning-based methodologies for dubbing will translate unknown language audio into meaningful words. This chapter provides a detailed study on the recent deep learning models in literature for dubbing. Deep learning models for dubbing can be categorized based on the feature representation as audio, visual, and multimodal features. More models are prevailing for English language, and a few techniques are available for Indian languages. In this chapter, the authors provide an end-to-end solution to predict the lip movements and translate them into natural language. This study also covers the recent enhancements in deep learning for natural language processing. Also, the future directions for the automated dubbing process domain are discussed.
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