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American sign language fingerspelling recognition with phonological feature-based tandem models | IEEE Conference Publication | IEEE Xplore

American sign language fingerspelling recognition with phonological feature-based tandem models


Abstract:

We study the recognition of fingerspelling sequences in American Sign Language from video using tandem-style models, in which the outputs of multilayer perceptron (MLP) c...Show More

Abstract:

We study the recognition of fingerspelling sequences in American Sign Language from video using tandem-style models, in which the outputs of multilayer perceptron (MLP) classifiers are used as observations in a hidden Markov model (HMM)-based recognizer. We compare a baseline HMM-based recognizer, a tandem recognizer using MLP letter classifiers, and a tandem recognizer using MLP classifiers of phonological features. We present experiments on a database of fingerspelling videos. We find that the tandem approaches outperform an HMM-based baseline, and that phonological feature-based tandem models outperform letter-based tandem models.
Date of Conference: 02-05 December 2012
Date Added to IEEE Xplore: 31 January 2013
ISBN Information:
Conference Location: Miami, FL, USA

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