A Signer-Independent Sign Language Recognition System Based on SOFM/HMM
Fang Gao · Chinese Journal of Computers · 2002
Sign language recognition has emerged as one of the most important research areas in the field of human computer interaction. The aim of sign language recognition is to provide an efficient and accurate mechanism to transcribe sign language into text or speech so that communication between deaf and hearing society becomes more convenient. State of the art sign language recognition should be able to solve the signer independent problem for practical applications. This paper analyzes the features of signer independent sign language: (1) the convergence difficulty caused by mass data and noticeable distinctions between different people data. (2) the urgent need to extract common features from different people data. Aiming at these features, the SOFM/HMM model presented in this paper combines the powerful feature extraction performances of self organizing feature maps (SOFM) with excellent temporal processing properties of hidden Markov models (HMM) within a novel scheme. Each SOFM eigenveter centroid is regarded as one of the components in the state of HMM which construct the state probability density function in terms of the weighted sum. The model parameters can be re estimated through the Expectation Maximization (EM) algorithm. When the proposed model is applied to signer independent Chinese Sign Language (CSL) recognition with a vocabulary of 208 signs, 95.3% recognition rate is obtained in the registered test (Reg.) and 88.2% in the unregistered test (UnReg.). Meanwhile, results from the conventional HMM system are provided as comparison. Experimental results show the SOFM/HMM system increases the recognition accuracy by 5% than conventional HMM one.