DeepASL: Enabling Ubiquitous and Non-Intrusive Word and Sentence-Level Sign Language Translation

There is an undeniable communication barrier between deaf people and people with normal hearing ability. Although innovations in sign language translation technology aim to tear down this communication barrier, the majority of existing sign language translation systems are either intrusive or constrained by resolution or ambient lighting conditions. Moreover, these existing systems can only perform single-sign ASL translation rather than sentence-level translation, making them much less useful in daily-life communication scenarios. In this work, we fill this critical gap by presenting DeepASL, a transformative deep learning-based sign language translation technology that enables ubiquitous and non-intrusive American Sign Language (ASL) translation at both word and sentence levels. DeepASL uses infrared light as its sensing mechanism to non-intrusively capture the ASL signs. It incorporates a novel hierarchical bidirectional deep recurrent neural network (HB-RNN) and a probabilistic framework based on Connectionist Temporal Classification (CTC) for word-level and sentence-level ASL translation respectively. To evaluate its performance, we have collected 7,306 samples from 11 participants, covering 56 commonly used ASL words and 100 ASL sentences. DeepASL achieves an average 94.5% word-level translation accuracy and an average 8.2% word error rate on translating unseen ASL sentences. Given its promising performance, we believe DeepASL represents a significant step towards breaking the communication barrier between deaf people and hearing majority, and thus has the significant potential to fundamentally change deaf people's lives

A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute

first_pagesettingsOrder Article Reprints Open AccessArticle A Novel Machine Learning Based Two-Way Communication System for Deaf and Mute by Muhammad Imran Saleem 1,2,*ORCID,Atif Siddiqui 3ORCID,Shaheena Noor 4ORCID,Miguel-Angel Luque-Nieto 1,2ORCID andPablo Otero 1,2ORCID 1 Telecommunications Engineering School, University of Malaga, 29010 Malaga, Spain 2 Institute of Oceanic Engineering Research, University of Malaga, 29010 Malaga, Spain 3 Airbus Defence and Space, UK 4 Department of Computer Engineering, Faculty of Engineering, Sir Syed University of Engineering and Technology, Karachi 75300, Pakistan * Author to whom correspondence should be addressed. Appl. Sci. 2023, 13(1), 453; https://doi.org/10.3390/app13010453 Received: 12 November 2022 / Revised: 22 December 2022 / Accepted: 26 December 2022 / Published: 29 December 2022 Download Browse Figures Versions Notes Abstract Deaf and mute people are an integral part of society, and it is particularly important to provide them with a platform to be able to communicate without the need for any training or learning. These people rely on sign language, but for effective communication, it is expected that others can understand sign language. Learning sign language is a challenge for those with no impairment. Another challenge is to have a system in which hand gestures of different languages are supported. In this manuscript, a system is presented that provides communication between deaf and mute (DnM) and non-deaf and mute (NDnM). The hand gestures of DnM people are acquired and processed using deep learning, and multiple language support is achieved using supervised machine learning. The NDnM people are provided with an audio interface where the hand gestures are converted into speech and generated through the sound card interface of the computer. Speech from NDnM people is acquired using microphone input and converted into text. The system is easy to use and low cost. (...)This research has been partially funded by Universidad de Málaga, Málaga, Spain