Toward HMM based machine translation for ASL

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.

[1]  Scott K. Liddell,et al.  American Sign Language: The Phonological Base , 2013 .

[2]  Mohamed Jemni,et al.  Sign Language MMS to Make Cell Phones Accessible to the Deaf and Hard-of-hearing Community , 2007, CVHI.

[3]  Dimitris N. Metaxas,et al.  ASL recognition based on a coupling between HMMs and 3D motion analysis , 1998, Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271).

[4]  Eun-Jung Holden,et al.  Dynamic Fingerspelling Recognition using Geometric and Motion Features , 2006, 2006 International Conference on Image Processing.

[5]  Mohamed Jemni,et al.  Mobile sign language translation system for deaf community , 2012, W4A.

[6]  Dimitris N. Metaxas,et al.  Adapting hidden Markov models for ASL recognition by using three-dimensional computer vision methods , 1997, 1997 IEEE International Conference on Systems, Man, and Cybernetics. Computational Cybernetics and Simulation.

[7]  David Windridge,et al.  A Linguistic Feature Vector for the Visual Interpretation of Sign Language , 2004, ECCV.

[8]  Stephan Liwicki,et al.  Automatic recognition of fingerspelled words in British Sign Language , 2009, 2009 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[9]  Wen Gao,et al.  An approach based on phonemes to large vocabulary Chinese sign language recognition , 2002, Proceedings of Fifth IEEE International Conference on Automatic Face Gesture Recognition.

[10]  Wen Gao,et al.  Sign Language Recognition Based on HMM/ANN/DP , 2000, Int. J. Pattern Recognit. Artif. Intell..

[11]  Mohamed Jemni,et al.  A System to Make Signs Using Collaborative Approach , 2008, ICCHP.

[12]  Mohamed Jemni,et al.  3D Motion Trajectory Analysis Approach to Improve Sign Language 3D-based Content Recognition , 2012, INNS-WC.

[13]  Robyn A. Owens,et al.  Australian sign language recognition , 2005, Machine Vision and Applications.

[14]  Hermann Hienz,et al.  HMM-Based Continuous Sign Language Recognition Using Stochastic Grammars , 1999, Gesture Workshop.