Computer Assistive Technologies for Physically and Cognitively Challenged Users

Augmentative and Alternative Communication/ Hearing Impairments

Author(s): Jestin Joy*, Kannan Balakrishnan and M Sreeraj

Pp: 117-134 (18)

DOI: 10.2174/9789815079159123020008

* (Excluding Mailing and Handling)


Data-driven technologies aid in effective communication for deaf people. Research on sign language recognition, sign language generation and tools based on them is going at a fast pace. With the easy availability of depth sensors, specialized data sets, efficient machine learning algorithms, and computational capabilities provided by specialized hardware, the development of efficient data science-based solutions for deaf users and people with difficulty in hearing is possible now. This chapter focuses on recent research on Automatic Sign Language Recognition (ASLR), Sign Language Production (SLP) and tools based on them. A major focus of this chapter is research and tools using Sign Languages since they are the most commonly used communication medium by deaf people. Research on sign languages from different parts of the world as well as the effectiveness of Machine Learning techniques for ASLR and SLP, are discussed in detail.

Keywords: Augmentative and Alternative Communication (AAC), Automatic Sign Language Recognition (ASLR), Avatar, Corpus, Deaf, Depth Sensors, Generative Adversarial Network (GAN), Gesture, Indian Sign Language (ISL), Kinect, Leap Motion, Machine Learning, Neural Network, Pose, Recurrent Neural Network(RNN), Recognition, Sign Language, Sign Language generation, Sign Language Production (SLP), Vision, Variational AutoEncoder (VAE).

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