Abstract
Conference Title: 2013 Fourth International Conference on Information and Communication Technology and Accessibility (ICTA) Conference Start Date: 2013, Oct. 24 Conference End Date: 2013, Oct. 26 Conference Location: Hammamet, Tunisia HMM-based models are widely used in many fields such as pattern recognition, speech recognition or Part-of-speech tagging. However, A HMM can be considered as a simplest dynamic Bayesian network. This network allows us to design a probabilistic graphical model that can be used in machine translation field especially for sign language machine translation. In this paper, we present a Bayesian Learning based method to train the alignment between a simple GLOSS form and a more complicated GLOSS form using sign language specificities such as space locative and classifier predicates. [PUBLICATION ABSTRACT]