Genetically optimised feedforward neural networks for speaker identification

被引：0

作者：

Price, RC ^{[1
]}

Willmore, JP ^{[1
]}

Roberts, WJJ ^{[1
]}

Zyga, KJ ^{[1
]}

机构：

[1] Def Sci & Technol Org, Div Informat Technol, Salisbury, SA 5108, Australia

来源：

KES'2000: FOURTH INTERNATIONAL CONFERENCE ON KNOWLEDGE-BASED INTELLIGENT ENGINEERING SYSTEMS & ALLIED TECHNOLOGIES, VOLS 1 AND 2, PROCEEDINGS | 2000年

关键词：

D O I：

暂无

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The problem of indentfying a speaker from a given utterance has been conventionally addressed using techniques such as Gaussian Mixture Models (GMM's) that model the characteristics of a known speaker via means and covariances. In this paper we compare the performance of a genetically optimised neural network speaker identification system versus the conventional approach of GMM's. The rest data used in the experiments was the data used for the 1996 National Institute for Standards Technology (NIST) evaluation of speaker identification systems.

引用

页码：479 / 482

页数：4

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