Unsupervised Discovery of Sign Terms by K-Nearest Neighbours Approach

In order to utilize the large amount of unlabeled sign language resources, unsupervised learning methods are needed. Motivated by the successful results of unsupervised term discovery (UTD) in spoken languages, here we explore how to apply similar methods for sign terms discovery. Our goal is to find the repeating terms from continuous sign videos without any supervision. Using visual features extracted from RGB videos, we show that a k-nearest neighbours based discovery algorithm designed for speech can also discover sign terms. We also run experiments using a baseline UTD algorithm and comment on their differences.