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Unsupervised feature learning for visual sign language identification

Gebre, B. G., Crasborn, O., Wittenburg, P., Drude, S., & Heskes, T. (2014). Unsupervised feature learning for visual sign language identification. In Proceedings of the 52nd Annual Meeting of the Association for Computational Linguistics: Vol 2 (pp. 370-376). Redhook, NY: Curran Proceedings.
Prior research on language identification focused primarily on text and speech. In this paper, we focus on the visual modality and present a method for identifying sign languages solely from short video samples. The method is trained on unlabelled video data (unsupervised feature learning) and using these features, it is trained to discriminate between six sign languages (supervised learning). We ran experiments on video samples involving 30 signers (running for a total of 6 hours). Using leave-one-signer-out cross-validation, our evaluation on short video samples shows an average best accuracy of 84%. Given that sign languages are under-resourced, unsupervised feature learning techniques are the right tools and our results indicate that this is realistic for sign language identification.
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