The ATIS sign language corpus

Systems that automatically process sign language rely on appropriate data. We therefore present the ATIS sign language corpus that is based on the domain of air travel information. It is available for five languages, English, German, Irish sign language, German sign language and South African sign language. The corpus can be used for different tasks like automatic statistical translation and automatic sign language recognition and it allows the specific modelling of spatial references in signing space

Sign language recognition with transformer networks

Sign languages are complex languages. Research into them is ongoing, supported by large video corpora of which only small parts are annotated. Sign language recognition can be used to speed up the annotation process of these corpora, in order to aid research into sign languages and sign language recognition. Previous research has approached sign language recognition in various ways, using feature extraction techniques or end-to-end deep learning. In this work, we apply a combination of feature extraction using OpenPose for human keypoint estimation and end-to-end feature learning with Convolutional Neural Networks. The proven multi-head attention mechanism used in transformers is applied to recognize isolated signs in the Flemish Sign Language corpus. Our proposed method significantly outperforms the previous state of the art of sign language recognition on the Flemish Sign Language corpus: we obtain an accuracy of 74.7% on a vocabulary of 100 classes. Our results will be implemented as a suggestion system for sign language corpus annotation

Discourses Of Prejudice In The professions: The Case Of Sign Languages

There is no evidence that learning a natural human language is cognitively harmful to children. To the contrary, multilingualism has been argued to be beneficial to all. Nevertheless, many professionals advise the parents of deaf children that their children should not learn a sign language during their early years, despite strong evidence across many research disciplines that sign languages are natural human languages. Their recommendations are based on a combination of misperceptions about (1) the difficulty of learning a sign language, (2) the effects of bilingualism, and particularly bimodalism, (3) the bona fide status of languages that lack a written form, (4) the effects of a sign language on acquiring literacy, (5) the ability of technologies to address the needs of deaf children and (6) the effects that use of a sign language will have on family cohesion. We expose these misperceptions as based in prejudice and urge institutions involved in educating professionals concerned with the healthcare, raising and educating of deaf children to include appropriate information about first language acquisition and the importance of a sign language for deaf children. We further urge such professionals to advise the parents of deaf children properly, which means to strongly advise the introduction of a sign language as soon as hearing loss is detected

Hand in hand: automatic sign Language to English translation

In this paper, we describe the first data-driven automatic sign-language-to- speech translation system. While both sign language (SL) recognition and translation techniques exist, both use an intermediate notation system not directly intelligible for untrained users. We combine a SL recognizing framework with a state-of-the-art phrase-based machine translation (MT) system, using corpora of both American Sign Language and Irish Sign Language data. In a set of experiments we show the overall results and also illustrate the importance of including a vision-based knowledge source in the development of a complete SL translation system

Perceptually optimised sign language video coding

Mobile video telephony will enable deaf people to communicate in their own language, sign language. At low bit rates coding of sign language video is challenging due the high levels of motion and the need to maintain good image quality to aid with understanding. This paper presents perceptually optimised coding of sign language video at low bit rates. The proposed optimisations are based on an eye-tracking study that we have conducted with the aim of characterising the visual attention of sign language viewers. Analysis and results of this study and two coding methods, one using MPEG-4 video objects and the second using foveation filtering, are presented. Results with foveation filtering are promising, offering a considerable decrease in bit rate in a manner compatible with the visual attention patterns of deaf people, as these were recorded in the eye tracking study

Alphabet Sign Language Recognition Using Leap Motion Technology and Rule Based Backpropagation-genetic Algorithm Neural Network (Rbbpgann)

Sign Language recognition was used to help people with normal hearing communicate effectively with the deaf and hearing-impaired. Based on survey that conducted by Multi-Center Study in Southeast Asia, Indonesia was on the top four position in number of patients with hearing disability (4.6%). Therefore, the existence of Sign Language recognition is important. Some research has been conducted on this field. Many neural network types had been used for recognizing many kinds of sign languages. However, their performance are need to be improved. This work focuses on the ASL (Alphabet Sign Language) in SIBI (Sign System of Indonesian Language) which uses one hand and 26 gestures. Here, thirty four features were extracted by using Leap Motion. Further, a new method, Rule Based-Backpropagation Genetic Al-gorithm Neural Network (RB-BPGANN), was used to recognize these Sign Languages. This method is combination of Rule and Back Propagation Neural Network (BPGANN). Based on experiment this pro-posed application can recognize Sign Language up to 93.8% accuracy. It was very good to recognize large multiclass instance and can be solution of overfitting problem in Neural Network algorithm

Review of the book Deaf around the World: The impact of language / ed. by Mathur & Napoli

(first paragraph) Since its advent half a century ago, the field of sign language linguistics has had close ties to education and the empowerment of deaf communities, a union that is fittingly celebrated by Deaf around the world: The impact of language. With this fruitful relationship in mind, sign language researchers and deaf educators gathered in Philadelphia in 2008, and in the volume under review, Gaurav Mathur & Donna Jo Napoli (henceforth M&N) present a selection of papers from this conference, organised in two parts: ‘Sign languages: Creation, context, form’, and ‘Social issues/civil rights ’..