Using N-Gram Analytics to Improve Automatic Fingerspelling Generation

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.