  In this thesis, 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, and recognizing it 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 propose several types of recognition approaches, and explore the
signer variation problem. 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
8% letter error rates. The signer-independent setting is much more challenging,
but with neural network adaptation we achieve up to 17% letter error rates.
