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This paper reports on work in synthesizing the finger alphabet of Swiss German Sign Language (Deutschschweizerische Gebärdensprache, DSGS) as a first step towards a fingerspelling learning tool for this language. Sign language synthesis is an instance of automatic sign language processing, which in turn forms part of natural language processing (NLP). The contribution of this paper is twofold: Firstly, the process of creating a set of hand postures and transitions for the DSGS finger alphabet is explained, and secondly, the results of a study assessing the comprehensibility of the resulting animations are reported. The comprehension rate of the signing avatar was highly satisfactory at 90.06%.

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Fingerspelling recognition is one of the last skills acquired, due to the complex nature of fingerspelling and a lack of display technology that is sufficiently natural for recognition practice. This paper describes a corpus-based study utilizing an n-gram extension to ELAN to gain a deeper understanding of deletion and coarticulation in fingerspelling. The analysis shows that coarticulation and deletion increase with fingerspelling speed and that deletions form an increasing percentage of the modifications at shorter durations. Insights from the study informed strategies to improve current avatar-based fingerspelling generation.

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