An improved adaptation algorithm for signer-independent sign language recognition

Abstract

Sign language recognition is a technology that can present sign language in a understandable form, in order to achieve barrier-free communication between deaf and normal. To solve the differences in sign language data issues and the lack of training samples of manpower caused by low recognition of non-specific language, presents MLLR\MAP adaptive progressive non-specific integrated manpower language recognition framework. This approach optimises the division MLLR regression class to provide more accurate initial MAP model, which give full play to the rapidity and the MAP MLLR progressive. Then introduced MCE model parameter estimation algorithm to compensate for the limitations of the model parameters adaptive method to further reduce the system error rate and accelerate the recognition speed. Meanwhile, for the MCE algorithm computationally intensive problems proposed improvements. Experimental results show that the adaptive sign language data required for this algorithm is less than traditional MLLR and MAP methods, while improved average recognition rate by 15.6%.