A New Probabilistic Model for Recognizing Signs with Systematic Modulations

Abstract. This paper addresses an aspect of sign language (SL) recognition that has largely been overlooked in previous work and yet is integral to signed communication. It is the most comprehensive work to-date on recognizing complex variations in sign appearances due to grammatical processes (inflections) which systematically modulate the temporal and spatial dimensions of a root sign word to convey information in addition to lexical meaning. We propose a novel dynamic Bayesian network

• Type: Conference
• Year: 2007
• Where: AMFG
• Authors: Sylvie C. W. Ong, Surendra Ranganath
• Keywords: AMFG 2007, Biometrics, Comprehensive Work To-date, Hierarchical Hidden Markov Model, Root Sign Word