提高MFCC鲁棒性的方法  

Approach to improve robust performance of Mel frequency coefficient cepstral

作　　者：韩春光[1] 李华[1] 

机构地区：[1]宁波大红鹰职业技术学院现代应用技术系,浙江宁波315175

出　　处：《计算机工程与设计》2008年第1期160-162,180,共4页Computer Engineering and Design

基　　金：浙江省教育厅科研基金项目(20050099)

摘　　要：在噪声环境中,基于笔记本电脑录音的情况下,采用特征参数加窗的方法,以提高系统的噪声鲁棒性。在Matlab环境下,建立了基于高斯混合模型(GMM)的说话人辨认系统,并进行实验。通过对多种窗口的正识率比较,发现对美尔倒谱(MFCC)高阶参数的加窗提升,可以改善系统的鲁棒性。实验结果表明,采用加窗后的系统识别率得到了明显改善。An approach of the cepstal liftering window is used to improve robust of the speaker recognition system under the Notebook- PC condition in noise surrounding. A speaker identification system based on Gaussian mixture model （GMM） is applied to an experiment for the cepstral coefficient in Matlab. With compared different liftering windows in the result of right recognition rate, we found that the high cepstral term of MFCC make better robust performance in speaker recognition. The result shows that the performance of the system robust with liftering window is further improved.

关 键 词：美尔倒谱参数 说话人辨认 高斯混合模型 加窗提升 噪声鲁棒性 

分 类 号：TP391.42[自动化与计算机技术—计算机应用技术]