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arXiv · 1609.07876

Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation

Taehwan Kim·Jonathan Keane·Weiran Wang·Hao Tang·Jason Riggle·Gregory Shakhnarovich·Diane Brentari·Karen Livescu
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Abstract

We study the problem of recognizing video sequences of fingerspelled letters in American Sign Language (ASL). Fingerspelling comprises a significant but relatively understudied part of ASL. Recognizing fingerspelling is challenging for a number of reasons: It involves quick, small motions that are often highly coarticulated; it exhibits significant variation between signers; and there has been a dearth of continuous fingerspelling data collected. In this work we collect and annotate a new data set of continuous fingerspelling videos, compare several types of recognizers, and explore the problem of signer variation. Our best-performing models are segmental (semi-Markov) conditional random fields using deep neural network-based features. In the signer-dependent setting, our recognizers achieve up to about 92% letter accuracy. The multi-signer setting is much more challenging, but with neural network adaptation we achieve up to 83% letter accuracies in this setting.

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Taehwan Kim, Jonathan Keane, Weiran Wang, Hao Tang, Jason Riggle, Gregory Shakhnarovich, Diane Brentari, Karen Livescu. 2016-09-26. Lexicon-Free Fingerspelling Recognition from Video: Data, Models, and Signer Adaptation. https://arxiv.org/abs/1609.07876

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