摘要
针对语音信号特征参数LPCC和MFCC相结合后数据维数过高,导致识别器性能下降的问题,提出采用遗传算法对初始特征参数进行降维,来提高识别性能.首先提取语音信号的LPCC和MFCC,然后采用遗传算法对其进行特征降维,最后将得到的低维数据送入支持向量机进行识别.仿真实验结果表明,采用遗传算法进行特征降维与传统的PCA降维相比,识别率提高了12.2%,和初始特征相比识别率降低了1.23%,但是识别时间提高了4.5倍.
The dimensions are higher after combining MFCC with LPCC, which affect the performance of recognition. To solve the problem, genetic algorithm parameters for initial feature dimension reduction, to improve recognition performance. Firstly, we extract MFCC and LPCC of the speech signal. Then we reduce the dimensionality based on genetic algorithm. Finally, recognition is presented based on SVM with the low dimensional data. Simulation results show that compared to traditional PCA, the genetic algorithm recognition can increase the recognition rate by 12.2%. Meanwhile, recognition rate was reduced by 1.23% compared with the initial feature,but the recognition time increased 4.5 times.
出处
《计算机系统应用》
2016年第1期150-153,共4页
Computer Systems & Applications
