1. Tanmoy Bhattacharya Coordinator Equal Opportunity Cell University of Delhi tanmoy1@gmail.Com ICT for PwDs: with Special Reference to Indian Sign Language

2. Equal Opportunity Cell: University of Delhi Access Audit: 140 digitised reports Services: Reading, Braille printing, e-text conversion, recording for talking books, Transportation, ICT lab, assistive devices, Sign Language Interpreters Events: Inclusive Chess tournament, Sports events, Cultural Festival, National Disability Conference; Awareness Workshops in Colleges and Schools Skills Development Courses: English Communication, ICT for Blind and Mobility Impaired, News Reading and Cinematography, Sign Language Interpretation A, B and C Levels, Disability and Human Rights. Short- term Summer Courses on Computer literacy 070911 2 Bhattacharya/ USOF/ DOT

3. On Sign Language Sign languages create representations in the space in front of the signer (Signing Space) Due to the importance of vision, signed languages take advantage of spatial representations (he, she, it, etc.) The linguistic uniqueness of sign localisation is beyond doubt 070911 3 Bhattacharya/ USOF/ DOT

4. The Signing Space 070911 4 Bhattacharya/ USOF/ DOT

5. Time in Indian Sign Language: An Example 070911 5 Bhattacharya/ USOF/ DOT

6. SL Technology Text/ Speech-to-Sign  Voice recognition module (Speech Recogniser)  Conversion of sentence to fit the grammar of sign language (Inter-Language Translator)  3D Avatar Animation Module Sign-to-Text/ Speech  Sign Recognition: Video Signal input to large vocabulary speech recognition database  Automatic machine translation system to create a spoken language translation Problems : Simultaneousness; 3D body-centred Signing Space; Sub-word units 070911 6 Bhattacharya/ USOF/ DOT

7. Status of SL Recognition Systems 070911 Bhattacharya/ USOF/ DOT 7 “ Today’s Sign Language recognition is at about the stage where speech recognition was 20 years ago ” -- Thad Starner, Head of the Contextual Computing Group at the Georgia Institute of Technology

8. Challenges to ICT for Deaf 070911 Bhattacharya/ USOF/ DOT 8 The major barrier in using ICT for the Deaf is the assumption that English/ Hindi/ State language is their first language – their capabilities are often measured against understanding the written word Most effective ICT for the Deaf is visual rather than based on the written word or sound Text messages are limiting since it doesn’t convey emotions, voice inflections or body language (similarly Text-Speech systems) The smart phones with front-facing cameras for videoconferencing can be used for video chat but are too much of a bandwidth hog

9. Applications Currently Available or Under Research 070911 Bhattacharya/ USOF/ DOT 9 Video Chat Softwares Online Dictionaries Speech-Signal Translators Automated SL Generation System Smart Phone Applications

10. Video Chat Software 070911 10 Bhattacharya/ USOF/ DOT

11. ASL On-line Dictionary 070911 11 Bhattacharya/ USOF/ DOT

12. SiSi (Say It Sign It) 070911 Bhattacharya/ USOF/ DOT 12 IBM Research, Hursely, UK, 2007 Voice-Text-Sign

13. NHK Science & Technology Research laboratories, Japan 070911 13 Bhattacharya/ USOF/ DOT

14. Smart Phone Application 070911 14 Bhattacharya/ USOF/ DOT

15. Existing Infrastructure 070911 Bhattacharya/ USOF/ DOT 15 Schools, Common Service Centres, Primary Health Centres, Panchayats, Women’s Self Help Groups (SHGs), already covered under various USOF programmes Rural and Remote Areas Mobile Services of USOF: 500 districts in 27 states Rural Broadband Scheme: 8,61,459 Broadband connections; Scheme for Intra- District Networks on Bandwith Sharing with 2.5 Gb capacity

16. Future Directions 070911 Bhattacharya/ USOF/ DOT 16 Development of the ISL sign set Development of software for converting Hindi/ regional language words to ISL through online dictionary Interactive learning software using the NBT book series for shapes, measures, colours, time, money for 2-3 year olds; newspaper and adult education for Deaf adults Send video over both 3G and Wi-Fi networks at a very low bit rate Optimisation of compressed video signals by increasing image quality around the face and hands to bring data rate down Motion detection to identify whether a person is signing or not for extending battery life

17. Contact 070911 Bhattacharya/ USOF/ DOT 17 Tanmoy Bhattacharya Coordinator Equal Opportunity Cell DU-NTPC Foundation ICT Training Centre Tutorial Building, Arts Faculty University of Delhi Delhi 110007 Phone: 011-27662602 (Office) Email: tanmoy1@gmail.com; eoc@du.ac.intanmoy1@gmail.comeoc@du.ac.in Website: http://eoc.du.ac.inhttp://eoc.du.ac.in