计算机工程与应用2012，Vol.48Issue(8)：155-157,3.DOI:10.3778/j.issn.1002-8331.2012.08.044
改进的美尔倒谱系数用于说话人识别研究
Research on speaker recognition with improved MFCC
刘宏 1刘立群2
作者信息
- 1. 辽宁师范大学管理学院,辽宁大连116029
- 2. 沈阳师范大学计算中心,沈阳110034
- 折叠
摘要
Abstract
Mel-Frequency Cepstral Coefficients (MFCC) based on the human auditory system represents high recognition rate and strong power against noise compared with other features. However, due to the structure of its filter bank, it captures characteristics information more effectively in the lower frequency regions than the higher regions. Thus there must be some informations contained in the high frequency, which are missed. This work uses a new set of features by reversal of the filter bank structure which can make up the lack of MFCC. Considering the advantages of the two features MFCC and R-MFCC and using the Fisher criterion which is used to measure the recognition of various parameters, a new hybrid parameter is constructed through a combination of the Fisher criterion. Support vector machine as classifiers are adopted to identify speaker with MFCC, R-MFCC and the new hybrid parameter respectively. Experimental data shows that the new hybrid feature based on Fisher criterion is effective in raising the recognition rate of the speaker recognition.关键词
说话人识别/反美尔倒谱系数/Fisher准则/支持向量机Key words
speaker recognition/ reversal Mel-Frequency Cepstral Coefficients (MFCC)/ Fisher criterion/ support vector machine分类
信息技术与安全科学引用本文复制引用
刘宏,刘立群..改进的美尔倒谱系数用于说话人识别研究[J].计算机工程与应用,2012,48(8):155-157,3.
