Text-independent Speaker Recognition Using Radial Basis Function Network

被引：2

作者：

Yakovenko, Anton ^{[1
]}

Malychina, Galina ^{[1
]}

机构：

[1] Peter Great St Petersburg Polytech Univ, Inst Comp Sci & Technol, Polytech Skaya 21, St Petersburg 194021, Russia

来源：

ADVANCES IN NEURAL NETWORKS - ISNN 2016 | 2016年 / 9719卷

关键词：

Speaker recognition; Text-independence; Feature extraction; RBFNN;

D O I：

10.1007/978-3-319-40663-3_9

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Radial Basis Function Neural Network (RBFNN) is proposed as a solution to the text-independent speaker recognition problem. Recognition is based on estimation of sufficiently large set of acoustic features, construction of multidimensional histograms and approximation arbitrary distributions of the components of acoustic features with probability density functions (PDF), with possibility of wide shape variation. Proposed method allowed to reduce the probability of errors in the decision making.

引用

页码：74 / 81

页数：8

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