摘要
在说话者识别系统中,提取反映说话者个性的语音信号特征参数是系统的关键问题之一。研究并提取了几种重要的语音特征参数,包括线性预测倒谱系数、美尔倒谱系数、语音动态参数等,对这些参数进行了分析和比较,并研究了将多种参数组合使用对识别系统性能的影响。通过仿真和实验,证明混合参数识别方法能使话者识别系统的正确识别率有明显的提高。
In speaker recognition system, it抯 one of the key problems to extract the valid acoustic features that can represent speaker抯 characters. Several kinds of important features are studied and extracted, include linear prediction cepstrum coefficients (LPCC), mel-frequency cepstrum coefficients (MFCC), acoustic dynamic feature etc. These features are analyzed and compared, and the mixed features?effect on the performance of the recognition system is also researched. The simulation results show that the mixed features can obviously improve the recognition accuracy of the speaker recognition system.
出处
《系统仿真学报》
CAS
CSCD
2003年第9期1276-1278,共3页
Journal of System Simulation
关键词
说话者识别
动态特征
线性预测倒谱系数
美尔倒谱系数
仿真
speaker recognition
dynamic feature
linear prediction cepstrum coefficients (LPCC)
mel-frequency cepstrum coefficients (MFCC)
simulation
