Distributed genetic algorithm for Gaussian mixture model based speaker identification

被引：1
|
作者：
Lung, SY [1 ]
机构：
[1] Chung Kuo Inst Technol, Dept Management Informat Syst, Taipei, Taiwan
来源：
PATTERN RECOGNITION | 2003年 / 36卷 / 10期
关键词：
speaker identification; distribution genetic algorithm; vector quantization; Markov random field;
D O I：
10.1016/S0031-3203(02)00375-8
中图分类号：
TP18 [人工智能理论];
学科分类号：
081104 ; 0812 ; 0835 ; 1405 ;
摘要：
This paper presents a novel algorithm for reducing the computational complexity of identifying a speaker within a Gaussian mixture speaker model (GMM) framework. We have combined distributed genetic algorithm (DGA) and the Markov random field (MRF) to avoid typical local minima for speaker vector quantization. To improve the computation efficiency, only unstable chromosomes corresponding to speaker data parts are evolved. Identification accuracies of 93% were achieved for 100 Mandarin speakers. (C) 2003 Pattern Recognition Society. Published by Elsevier Ltd. All rights reserved.
引用
收藏
页码：2479 / 2481
页数：3
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