基于SCHMM的手语识别方法的实验研究

Experimental Research on SCHMM for Sign Language Recognition

在近些年的手语识别工作中,HMM起到了重要的作用。提出将SCHMM用于手语识别,实验结果表明SCHMM优于离散隐马尔科夫模型(DHMM)和连续隐马尔科夫模型(CHMM),可以避开DHMM中因矢量量化造成的信息损失,在保证识别率的前提下降低模型的复杂性和运算量。

In recent years＇ works on sign language recognition, HMMs has played an important role. The statistical frame based on the HMM is the mainstream method in dynamic recognition domain recently; also is this article＇s basic theory. Presents a Semi-Continuous Hidden Markov Model for sign language recognition. Experiments show that SCHMM is prior to the DHMM and the CHMM. SCHMM avoids the information loss because of the vector estimate in DHMM, debases the complexity and the operations at the same recognition rate.