期刊名称：International Journal of Soft Computing & Engineering
电子版ISSN：2231-2307
出版年度：2011
卷号：1
期号：5
页码：330-335
出版社：International Journal of Soft Computing & Engineering
摘要：In this paper, robust features for text-independent speaker recognition has been explored. Through different experimental studies, it is demonstrated that the speaker related information can be effectively captured using Gaussian mixture Models (GMMs). The study on the effect of feature vector size for good speaker recognition demonstrates that, feature vector size in the range of 20-24 can capture speaker discrimination information effectively for a speech signal sampled at 16 kHz, it is established that the proposed speaker recognition system requires significantly less amount of data during both during training as well as in testing. The speaker recognition study using robust features for different mixtures components, training and test duration has been exploited. We demonstrate the speaker recognition studies on TIMIT database.
关键词：Gaussian Mixture Model ( GMM); MFCC;Robust Features; Speaker.
