Lip Feature Selection based on BPSO and SVM

摘要：

In speech synthesis system driven by visual speech, many irrelevant and redundant features will lessen the lipreading recognition result. So it is important to select lip features with stronger discriminate performance. Feature selection algorithm based on binary particle swarm optimization (BPSO) and support vector machines (SVM) is used to select the “optimal lip feature subset. Feature subset was generated randomly firstly, and then BPSO algorithms searched the feature space guided by the result of SVMs’ 10-fold crossover validation. After numbers of iteration, the best fitness feature subset was selected out as the vector of lip feature. Hidden Markov Model (HMM) with 4 states and 16 Gaussian mixture components is designed as a recognizer. Comparing with feature fusion based on concatenating, Experiment results in a small database for speaker-dependent case showed that the recognition rates with feature selection based on BPSO and SVM are improved by as much as 3.89％.

关键词： feature selection binary particle swarm optimization support vector machines normalized geometrical feature normalized DCT coefficients hidden Markov model

作者: Wang Mengjun

作者单位: School of Information Engineering,Hebei University of Technology,Tianjin 300401,China

会议类型: 国际会议

会议名称: 2011 10th International Conference on Electronic Measurement & Instruments(第十届电子测量与仪器国际会议 ICEMI2011)

会议地点: 成都

会议语种:英文

页码: 776-779

在线出版日期: 2011-08-16（万方平台首次上网日期，不代表论文的发表时间）