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Tanmoy Bhattacharya

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Tanmoy Bhattacharya. Coordinator. Equal Opportunity Cell. University of Delhi. tanmoy1_at_gmail.Com. ICT for PwDs:with Special Reference to Indian Sign Language – PowerPoint PPT presentation

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Title: Tanmoy Bhattacharya


1
ICT for PwDswith Special Reference to Indian
Sign Language
  • Tanmoy Bhattacharya
  • Coordinator
  • Equal Opportunity Cell
  • University of Delhi
  • tanmoy1_at_gmail.Com

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

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

4
The Signing Space
5
Time in Indian Sign Language An Example
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

7
Status of SL Recognition Systems
  • Todays 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
  • 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 doesnt
    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
  • Video Chat Softwares
  • Online Dictionaries
  • Speech-Signal Translators
  • Automated SL Generation System
  • Smart Phone Applications

10
Video Chat Software
11
ASL On-line Dictionary
12
SiSi (Say It Sign It)
  • IBM Research, Hursely, UK, 2007
  • Voice-Text-Sign

13
NHK Science Technology Research laboratories,
Japan
14
Smart Phone Application
15
Existing Infrastructure
  • Schools, Common Service Centres, Primary Health
    Centres, Panchayats, Womens 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
  • 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
  • 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_at_gmail.com eoc_at_du.ac.in
  • Website http//eoc.du.ac.in
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