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1. Complexity in sign languages

   https://doi.org/10.1515/lingvan-2021-0005  

   Malaia, Evie A.; Borneman, Joshua D.; Kurtoglu, Emre; Gurbuz, Sevgi Z.; Griffin, Darrin; Crawford, Chris; Gurbuz, Ali C. (October 2022, Linguistics Vanguard)

   Abstract Sign languages are human communication systems that are equivalent to spoken language in their capacity for information transfer, but which use a dynamic visual signal for communication. Thus, linguistic metrics of complexity, which are typically developed for linear, symbolic linguistic representation (such as written forms of spoken languages) do not translate easily into sign language analysis. A comparison of physical signal metrics, on the other hand, is complicated by the higher dimensionality (spatial and temporal) of the sign language signal as compared to a speech signal (solely temporal). Here, we review a variety of approaches to operationalizing sign language complexity based on linguistic and physical data, and identify the approaches that allow for high fidelity modeling of the data in the visual domain, while capturing linguistically-relevant features of the sign language signal. 

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2. Beyond the Sign Rules

   https://doi.org/10.5951/MTLT.2021.0230  

   Bishop, Jessica Pierson; Lamb, Lisa L.; Whitacre, Ian; Philipp, Randolph A.; Schappelle, Bonnie P. (March 2022, Mathematics Teacher: Learning and Teaching PK-12)

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3. Asymptotic Sign Coherence Conjecture

   https://doi.org/10.1080/10586458.2019.1650401  

   Gekhtman, Michael; Nakanishi, Tomoki (August 2019, Experimental Mathematics)
4. Sign Language Video Anonymization

   Xia, Zhaoyang; Chen, Yuxiao; Zhangli, Qilong; Huenerfauth, Matt; Neidle, Carol; Metaxas, Dimitris (June 2022, LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources)

   Deaf signers who wish to communicate in their native language frequently share videos on the Web. However, videos cannot preserve privacy—as is often desirable for discussion of sensitive topics—since both hands and face convey critical linguistic information and therefore cannot be obscured without degrading communication. Deaf signers have expressed interest in video anonymization that would preserve linguistic content. However, attempts to develop such technology have thus far shown limited success. We are developing a new method for such anonymization, with input from ASL signers. We modify a motion-based image animation model to generate high-resolution videos with the signer identity changed, but with preservation of linguistically significant motions and facial expressions. An asymmetric encoder-decoder structured image generator is used to generate the high-resolution target frame from the low-resolution source frame based on the optical flow and confidence map. We explicitly guide the model to attain clear generation of hands and face by using bounding boxes to improve the loss computation. FID and KID scores are used for evaluation of the realism of the generated frames. This technology shows great potential for practical applications to benefit deaf signers. 

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5. Sign Language Video Anonymization

   Xia, Zhaoyang; Chen, Yuxiao; Zhangli, Qilong; Huenerfauth, Matt; Neidle, Carol; Metaxas, Dimitri (January 2022, Proceedings of the LREC2022 10th Workshop on the Representation and Processing of Sign Languages: Multilingual Sign Language Resources)