文献类型：期刊文献

英文题名：Application of differential evolution optimization based Gaussian Mixture Models to speaker recognition

作者：Zhou, Hong[1]; Zhang, Jianhua[1]

机构：[1] School of Information Science and Engineering, East China University of Science and Technology, Shanghai 200237, China

年份：2014

起止页码：4297

外文期刊名：26th Chinese Control and Decision Conference, CCDC 2014

收录：EI(收录号：20143218039787)

语种：英文

外文关键词：Optimization - Parameter estimation - Probability density function - Speech recognition - Maximum principle - Object recognition - Gaussian distribution - Communication channels (information theory) - Evolutionary algorithms - Image segmentation

摘要：Voice-based speaker recognition technique can be used in the identification of speakers. In such manner, Gaussian Mixture Model (GMM) can provide voice feature vectors' probability density model. In this paper, the Akaike's Information Criterion (AIC) is used to identify structures of the GMM models. The GMM parameter optimization is done by the differential evolution (DE) algorithm. During the optimization, a new parametric method is applied aiming at ensuring the positive definite symmetry property of an arbitrary covariance matrix. Here, both the expectation-maximization (EM) and DE are applied to identify the GMM parameters of a simulated dataset, and the utility of DE is proved by comparing the performances of the two. Further, DE is used to identify parameters of the GMM of Speaker Dataset acquired by Information Processing Laboratory in Hokkaido University. Again, the good performances of DE demonstrate superiorities to the EM method. ? 2014 IEEE.