Sign language perception research for improving automatic sign language recognition

Current automatic sign language recognition (ASLR) seldom uses perceptual knowledge about the recognition of sign language. Using such knowledge can improve ASLR because it can give an indication which elements or phases of a sign are important for its meaning. Also, the current generation of data-driven ASLR methods has shortcomings which may...

A Hybrid Approach to Sign Language Recognition (extended abstract)

Methods commonly used for speech and sign language recognition often rely on outputs of Hidden Markov Models (HMM) or Dynamic TimeWarping (DTW) for classification, which aremerely factorized observation likelihoods. Instead, we propose to use Statistical DTW (SDTW) only for warping, while classifying the synchronized features with either of two...

Sign Language Recognition by Combining Statistical DTW and Independent Classification

To recognize speech, handwriting, or sign language, many hybrid approaches have been proposed that combine Dynamic Time Warping (DTW) or Hidden Markov Models (HMMs) with discriminative classifiers. However, all methods rely directly on the likelihood models of DTW/HMM. We hypothesize that time warping and classification should be separated...