Representing Plains Indian Sign Language

Author(s):  
Brian Hochman
Gesture ◽  
2014 ◽  
Vol 14 (3) ◽  
pp. 263-296 ◽  
Author(s):  
Luke Fleming

With the exception of Plains Indian Sign Language and Pacific Northwest sawmill sign languages, highly developed alternate sign languages (sign languages typically employed by and for the hearing) share not only common structural linguistic features, but their use is also characterized by convergent ideological commitments concerning communicative medium and linguistic modality. Though both modalities encode comparable denotational content, speaker-signers tend to understand manual-visual sign as a pragmatically appropriate substitute for oral-aural speech. This paper suggests that two understudied clusters of alternate sign languages, Armenian and Cape York Peninsula sign languages, offer a general model for the development of alternate sign languages, one in which the gesture-to-sign continuum is dialectically linked to hypertrophied forms of interactional avoidance up-to-and-including complete silence in the co-presence of affinal relations. These cases illustrate that the pragmatic appropriateness of sign over speech relies upon local semiotic ideologies which tend to conceptualize the manual-visual linguistic modality on analogy to the gestural communication employed in interactional avoidance, and thus as not counting as true language.


2019 ◽  
Vol 7 (2) ◽  
pp. 43
Author(s):  
MALHOTRA POOJA ◽  
K. MANIAR CHIRAG ◽  
V. SANKPAL NIKHIL ◽  
R. THAKKAR HARDIK ◽  
◽  
...  

Author(s):  
Sukhendra Singh ◽  
G. N. Rathna ◽  
Vivek Singhal

Introduction: Sign language is the only way to communicate for speech-impaired people. But this sign language is not known to normal people so this is the cause of barrier in communicating. This is the problem faced by speech impaired people. In this paper, we have presented our solution which captured hand gestures with Kinect camera and classified the hand gesture into its correct symbol. Method: We used Kinect camera not the ordinary web camera because the ordinary camera does not capture its 3d orientation or depth of an image from camera however Kinect camera can capture 3d image and this will make classification more accurate. Result: Kinect camera will produce a different image for hand gestures for ‘2’ and ‘V’ and similarly for ‘1’ and ‘I’ however, normal web camera will not be able to distinguish between these two. We used hand gesture for Indian sign language and our dataset had 46339, RGB images and 46339 depth images. 80% of the total images were used for training and the remaining 20% for testing. In total 36 hand gestures were considered to capture alphabets and alphabets from A-Z and 10 for numeric, 26 for digits from 0-9 were considered to capture alphabets and Keywords. Conclusion: Along with real-time implementation, we have also shown the comparison of the performance of the various machine learning models in which we have found out the accuracy of CNN on depth- images has given the most accurate performance than other models. All these resulted were obtained on PYNQ Z2 board.


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