Hamburg Notation System

Abstract: Objective: Development of Indian Sign Language video dictionary is essential in the today’s world of computerization. Though a lot of human video sign language dictionaries are available, we aim to develop the Indian Sign Language dictionary using synthetic animation which uses the computer generated cartoon rather than real human. Methods/Statistical Analysis: Sign Language cannot be spoken or written unlike other languages like English, Punjabi, Hindi, etc. The most commonly used words in Indian Sign Language are categorized and then these words are converted into the sign language writing notation (HamNoSys - Hamburg Notation System). This HamNoSys notation is then converted into SiGML (Signing Gesture Markup Language) using which the synthetic animation (using a computer generated cartoon) of the sign is generated. Findings: The synthetic animations are better as compared to human videos in terms of memory consumption, standardization, and flexibility. Synthetic animations can be modified as per the requirement whereas the human videos cannot be modified. The only drawback that seem is, these synthetic animations may lack the natural non-manual component of sign. Applications/Improvements: The synthetic dictionary created in this work can be used for translation system in which spoken or written sentence can be converted into the sign language animation. The dictionary created can be used to education to hard of hearing people. Display boards can be created for displaying the important messages in Indian sign language at the public gathering.

Abstract: The arabic sign language (ArSL) is the natural language of the deaf community in Arabic countries. ArSL suffers from a lack of resources such as unified dictionaries and corpora. In this work, a dictionary of Arabic language to ArSL has been constructed as a part of a translation system. The Arabic words are converted into hamburg notation system (HamNoSys) using eSign editor Software. HamNoSys was used to create manual parameters (handshape, hand orientation, hand location, and hand movement), while non-manual parameters (facial expressions, shoulder raising, mouthing gesture, head tilting, and body movement) added by using (mouth, face, and limbs) in the eSign editor software. The sign then converted to the sign gesture markup language (SiGML) file, and later 3D avatar interprets the SiGML file scripts to the animated sign. The constructed dictionary has three thousand signs; therefore, it can be adopted for the translation system in which written text can be transformed into sign language and can be utilized for the education of deaf people. The dictionary will be available as a free resource for researchers. It is hard and time-consuming work, but it is an essential step in machine translation of whole Arabic text to ArSL with 3D animations.

Abstract: This paper discusses the use of the HamNoSys notation (Hamburg Notation System for Sign Languages) for the transcription of children’s signing. The notation system will be briefly described and some former descriptions of the acquisition of sign language phonology presented. The project in which HamNoSys was used is then described briefly followed by a description of the problems encountered while using the notation. Furthermore some proposals as to how to further develop the notation will be made. In conclusion the instrument can be said to be useful and, especially if revised, will be invaluable in further research.

Abstract: The Prosodic model of phonology (Brentari 1998) implies that all signs in any sign language have prosodic and inherent features. This dichotomy (movement feature vs. all other features) occurs to some extent in all phonological theories. The idea derives from Liddell & Johnson’s (1994) Movement-Hold model, where they proposed that movements can be in most cases derived from the knowledge of holds and their relative order, and that it is sufficient to describe in-detail only holds. Therefore, when it comes to describing phonemic inventories of a particular sign language, researchers focus on the building of separate phonemic inventories for each of the inherent features (or for features of holds) (Channon & Hulst 2011), namely handshape, location, and orientation (e.g. van der Kooij (2002) for Sign Language of the Netherlands (NGT) – only handshapes inventory, Nyst (2007) for Adamorobe Sign Language (AdaSL) – handshapes and locations inventories, etc.). This research focuses on handshapes inventory for Russian Sign Language (RSL). First, I automatically extract positions without movement (i.e. holds) using an algorithm developed on the basis of Borstell’s (2018) script. Then I manually annotate holds for the handshapes with respect to Hamburg Notation System (HamNoSys; Hanke 2004) and describe resulting phonetic handshapes inventory for RSL, comparing this data with other sign languages. The last but not the least, the enventory of phonemic hanshapes for RSL is derived from the phonetic one under van der Kooij’s (2002) model of phonology.