期刊名称：International Journal on Computer Science and Engineering
印刷版ISSN：2229-5631
电子版ISSN：0975-3397
出版年度：2011
卷号：3
期号：02
页码：586-593
出版社：Engg Journals Publications
摘要：In this paper, through different experimental studies it is demonstrated that the excitation component of speech can be exploited for text independent gender identification system. Linear prediction (LP) residual is used as a representation of excitation information in speech. The speakerspecific information in the excitation of voiced speech is captured using Gaussian Mixture Model (GMM). The decrease in the error during training and recognizing correct gender during testing demonstrates that the excitation component of speech contains speaker-specific information and is indeed being captured by GMM. It is demonstrated that the proposed gender identification system using excitation information requires significantly less amount of data both during training as well as in testing, compared to the other gender identification systems. A gender identification study using source feature for different Mixtures Components, train and test duration has been exploited. We demonstrate the gender identification studies on TIMIT database.
关键词：Gender;Gaussian Mixture Model (GMM); LPC; MFCC.
