Extension of hidden markov model for recognizing large vocabulary of
sign language
release_wp32trwimrfx5b2a65okkch47a
by
Maher Jebali, Patrice Dalle, Mohamed Jemni
2013
Abstract
Computers still have a long way to go before they can interact with users in
a truly natural fashion. From a users perspective, the most natural way to
interact with a computer would be through a speech and gesture interface.
Although speech recognition has made significant advances in the past ten
years, gesture recognition has been lagging behind. Sign Languages (SL) are the
most accomplished forms of gestural communication. Therefore, their automatic
analysis is a real challenge, which is interestingly implied to their lexical
and syntactic organization levels. Statements dealing with sign language occupy
a significant interest in the Automatic Natural Language Processing (ANLP)
domain. In this work, we are dealing with sign language recognition, in
particular of French Sign Language (FSL). FSL has its own specificities, such
as the simultaneity of several parameters, the important role of the facial
expression or movement and the use of space for the proper utterance
organization. Unlike speech recognition, Frensh sign language (FSL) events
occur both sequentially and simultaneously. Thus, the computational processing
of FSL is too complex than the spoken languages. We present a novel approach
based on HMM to reduce the recognition complexity.
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