摘要
为了提高语音识别系统的鲁棒性,提出一种基于GBFB(spectro-temporal Gabor filter bank)的声学特征提取方法,并通过分块PCA算法对高维的GBFB特征进行降维处理,最后在多个相同噪音环境对GBFB特征以及常用的GFCC,MFCC,LPCC等特征进行抗噪性能对比,与GFCC相比GBFB特征的识别率提高了5.35%,与MFCC特征相比提升了7.05%,比LPCC特征识别的基线低9个分贝。实验结果表明,在噪音环境下与传统的GFCC、MFCC以及LPCC等特征相比GBFB特征有更优越的鲁棒性。
In order to improve the robustness of speech recognition system,a method of extracting the acoustic features based on GBFB（spectro-temporal Gabor filter bank） is proposed,and the dimension of the GBFB is reduced by the block PCA algorithm.Finally,the feature of GBFB are compared with the feature of GFCC,MFCC and LPCC in different noise environments. The recognition rate of GBFB features is 5. 35% better than GFCC features,the recognition rate of GBFB features is 7. 05% better than MFCC features. Moreover,GBFB features are 9 d B lower than the LPCC recognition base. The experimental results show that the GBFB features exhibit better robustness than the traditional features of GFCC,MFCC and LPCC in the noisy environment.
作者
缑新科
徐高鹏
GOU Xin-ke;XU Gao-peng(College of Electrical and Information Engineering,Lanzhou University of Technology-,Lanzhou 730050,China;Key Laboratory of Gansu Advanced Control for Industrial Processes,Lanzhou 730050,China;National Experimental Teaching Demonstration Center of Electrical and Control Engineering,Lanzhou University of Technology,Lanzhou 730050,China)
出处
《计算机与现代化》
2018年第5期20-24,共5页
Computer and Modernization
