Lipreading Recognition Based on SVM and DTAK

To enhance recognition accuracy of isolated words identification with small samples in lipreading, SVM is first introduced to act as classifier in this paper. As SVM is based on structural risk minimization, it solves the problem of pattern recognition under small samples, on the other hand, it avoids the unreasonable hypothesis in traditional classifier. To meet the requirement of fixed input feature dimensionality in SVM, several input feature dimensionality normalization methods were discussed and compared. including 3-4-3 data segmenting method, HMM based method and DTAK(Dynamic Time Alignment Kernel) based method. Two experiments were performed on the bimodal database, In the first experiment different input feature normalization algorithm were compared on SVM. Experiments showed that DTAK based normalization achieved the best result. in the second experiments SVM was compared with HMM under different number samples occasion. Experimental results showed that SVM performs better than HMM under small samples.