摘要
针对MFCC不能得到高效的说话人识别性能的问题,提出了将时频特征与MFCC相结合的说话人特征提取方法。首先得到语音信号的时频分布,然后将时频域转换到频域再提取MFCC+MFCC作为特征参数,最后通过支持向量机来进行说话人识别研究。仿真实验比较了MFCC、MFCC+MFCC分别作为特征参数时语音信号与各种时频分布的识别性能,结果表明基于CWD分布的MFCC和MFCC的识别率可提高到95.7%。
Because MFCC can＇t reflect the dynamic characteristics of speech signal and their own non-stationary, a feature extraction method by combining time-frequency distribution with MFCC is proposed. First get time-frequency distribution of speech signal, and convert time-frequency domain into frequency domain, then extract MFCC＋MFCC as characteristic parameters. Finally speaker recognition uses the support vector machine. The simulation experiment compares recognition performance when MFCC and MFCC＋MFCC are respectively as characteristic parameters by speech signal and all kinds of time-frequency distribution. Results show that the speaker recognition performance using MFCC＋MFCC based on the CWD time-frequency distribution can be improved to 95.7%.
出处
《计算机系统应用》
2012年第4期189-192,178,共5页
Computer Systems & Applications
基金
国家自然科学基金(61075008)
