Feature Dimension Reduction Based on Genetic Algorithm For Mandarin Digit Recognition

摘要：

The dimensions are higher after combining Mel frequence cepstral coefficient with linear prediction cepstrum coefficient. In this paper, genetic algorithm is proposed to reduce the dimensions of the feature data to improve recognition performance of the system. First, extract Mel frequence cepstral coefficient and linear prediction cepstrum coefficient of the speech signal; then, reduce the dimensions of the feature data based on genetic algorithm; finally, the low dimensional data are sent into the support vector machine. Simulation results demonstrate that the recognition rate increases by 12.2％ using genetic algorithm compared with principle component analysis, recognition rate almost has no change compared with the initial characteristics and the recognition speed gets improved effectively.

关键词： mandarin digit recognition genetic algorithm support vector machine

作者: Gao Wen-xi Yu Feng-qin

作者单位: School of Internet of Things Engineering Jiangnan University Wuxi, China

会议类型: 国际会议

会议名称: 2011 4th International Congress on Image and Signal Processing(第四届图像与信号处理国际学术会议 CISP 2011)

会议地点: 上海

会议语种:英文

页码: 2768-2771

在线出版日期: 2011-10-15（万方平台首次上网日期，不代表论文的发表时间）