Learning Vector Quantization in text-independent Automatic Speaker Recognition

被引：2

作者：

Filgueiras, TE ^{[1
]}

Messina, RO ^{[1
]}

Cabral, EF ^{[1
]}

机构：

[1] Univ Sao Paulo, Escola Politec, Dept Elect Engn, Lab Comunicacoes & Sinais,Grp Comunicacao Homem M, Sao Paulo, Brazil

来源：

VTH BRAZILIAN SYMPOSIUM ON NEURAL NETWORKS, PROCEEDINGS | 1998年

关键词：

D O I：

10.1109/SBRN.1998.731010

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper it is reported a comparison among the Learning Vector Quantization (LVQ) and two other common approaches to text-independent speaker recognition, namely Gaussian Mixture Models (GMM) and Vector Quantization (Vg). Th, results shows that the neural approach is less efficient in terms of recognition scores than the GMM.

引用

页码：135 / 139

页数：5

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