Recognition of sign language subwords based on boosted hidden Markov models

Sign language recognition (SLR) plays an important role in human-computer interaction (HCI), especially for the convenient communication between deaf and hearing society. How to enhance the traditional hidden Markov models (HMM) based SLR is an important issue in the SLR community. And how to refine the boundaries of the classifiers to effectively characterize the property of spread-out of the training samples is another significant issue. In this paper, a new classification framework applying adaptive boosting (AdaBoost) strategy to continuous HMM (CHMM) training procedure at the subwords classification level for SLR is presented. The ensemble of multiple composite CHMMs for each subword trained in boosting iterations tends to concentrate more on the hard-to-classify samples so as to generate more complex decision boundary than that of the single HMM classifier. Experimental results on the vocabulary of frequently used Chinese sign language (CSL) subwords show that the proposed boost...

• Type: Conference
• Year: 2005
• Where: ICMI
• Authors: Liang-Guo Zhang, Xilin Chen, Chunli Wang, Yiqiang Chen, Wen Gao
• Keywords: ICMI 2005, Sign Language, Sign Language Recognition, SLR Community