scholarly journals 3D Avatar Approach for Continuous Sign Movement Using Speech/Text

2021 ◽  
Vol 11 (8) ◽  
pp. 3439
Author(s):  
Debashis Das Chakladar ◽  
Pradeep Kumar ◽  
Shubham Mandal ◽  
Partha Pratim Roy ◽  
Masakazu Iwamura ◽  
...  
Keyword(s):  
Language Learning ◽  
Sign Language ◽  
Language Processing ◽  
Hearing Impaired ◽  
Learning System ◽  
English Sentence ◽  
Impaired People ◽  
Indian Sign Language ◽  
Impaired Person ◽  
3D Avatar

Sign language is a visual language for communication used by hearing-impaired people with the help of hand and finger movements. Indian Sign Language (ISL) is a well-developed and standard way of communication for hearing-impaired people living in India. However, other people who use spoken language always face difficulty while communicating with a hearing-impaired person due to lack of sign language knowledge. In this study, we have developed a 3D avatar-based sign language learning system that converts the input speech/text into corresponding sign movements for ISL. The system consists of three modules. Initially, the input speech is converted into an English sentence. Then, that English sentence is converted into the corresponding ISL sentence using the Natural Language Processing (NLP) technique. Finally, the motion of the 3D avatar is defined based on the ISL sentence. The translation module achieves a 10.50 SER (Sign Error Rate) score.

Indian Sign Language Recognition on PYNQ Board

2020 ◽  
Vol 13 ◽  
Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal
Keyword(s):  
Sign Language ◽  
Hand Gesture ◽  
Language Recognition ◽  
Sign Language Recognition ◽  
Hand Gestures ◽  
Depth Images ◽  
Kinect Camera ◽  
Impaired People ◽  
Web Camera ◽  
Indian Sign Language

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.

scholarly journals Sentence Generation for Indian Sign Language Using NLP

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 01) ◽  
pp. 196-210
Author(s):  
Dr.P. Golda Jeyasheeli ◽  
N. Indumathi
Keyword(s):  
Deep Learning ◽  
Natural Language Processing ◽  
Sign Language ◽  
Language Processing ◽  
Normal Person ◽  
Human Language ◽  
Android Application ◽  
Sentence Generation ◽  
Normal People ◽  
Indian Sign Language

Nowadays the interaction among deaf and mute people and normal people is difficult, because normal people scuffle to understand the sense of the gestures. The deaf and dumb people find problem in sentence formation and grammatical correction. To alleviate the issues faced by these people, an automatic sign language sentence generation approach is propounded. In this project, Natural Language Processing (NLP) based methods are used. NLP is a powerful tool for translation in the human language and also responsible for the formation of meaningful sentences from sign language symbols which is also understood by the normal person. In this system, both conventional NLP methods and Deep learning NLP methods are used for sentence generation. The efficiency of both the methods are compared. The generated sentence is displayed in the android application as an output. This system aims to connect the gap in the interaction among the deaf and dumb people and the normal people.

An investigation into the relationship of foreign language learning motivation and sign language use among Deaf and hard of hearing Hungarians

2013 ◽  
Vol 51 (1) ◽  
pp. 1-22 ◽  
Author(s):  
Edit H. Kontra ◽  
Kata Csizér
Keyword(s):  
Foreign Language ◽  
Language Learning ◽  
Sign Language ◽  
Hard Of Hearing ◽  
Language Use ◽  
Foreign Language Learning ◽  
Hearing Impaired ◽  
Learning Motivation ◽  
Language Learning Motivation ◽  
The Relationship

Abstract The aim of this study is to point out the relationship between foreign language learning motivation and sign language use among hearing impaired Hungarians. In the article we concentrate on two main issues: first, to what extent hearing impaired people are motivated to learn foreign languages in a European context; second, to what extent sign language use in the classroom as well as outside school shapes their level of motivation. The participants in our research were 331 Deaf and hard of hearing people from all over Hungary. The instrument of data collection was a standardized questionnaire. Our results support the notion that sign language use helps foreign language learning. Based on the findings, we can conclude that there is indeed no justification for further neglecting the needs of Deaf and hard of hearing people as foreign language learners and that their claim for equal opportunities in language learning is substantiated.

scholarly journals Digital Sign Language App for Deaf Children

2021 ◽  
pp. 564-567
Author(s):  
Akib Khan ◽  
Rohan Bagde ◽  
Arish Sheikh ◽  
Asrar Ahmed ◽  
Rahib Khan ◽  
...  
Keyword(s):  
Bilingual Education ◽  
Sign Language ◽  
Hearing Impaired ◽  
Deaf Children ◽  
Local Language ◽  
The World ◽  
Impaired People ◽  
Inclusive Society

India has the highest population of hearing-impaired people in the world, numbering 18 million. Only 0.25 per cent of these numbers presently have access to bilingual education where knowledge of sign language is primary and that of a local language. Providing a platform that will help the entire hearing impaired community, their interpreters, families, and educators to understand sign language, will go a long way to facilitate easier conversations and exchange of ideas, thereby enabling a more inclusive society.

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