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应用科学学报 ›› 2002, Vol. 20 ›› Issue (2): 141-144.

• 论文 • 上一篇    下一篇

基于混合进化计算的GMM优化方法及其在说话人辨认中的应用

崔玉红, 胡光锐, 何旭明   

  1. 上海交通大学电子工程系, 上海 200030
  • 收稿日期:2001-04-14 修回日期:2001-06-28 出版日期:2002-06-30 发布日期:2002-06-30
  • 作者简介:崔玉红(1976-),女,湖北黄梅人,硕士生;胡光锐(1938-),男,上海人,教授,博导.

Optimization of GMM Based on Hybrid Evolutionary Algorithm and Its Application in Speaker Identification

CUI Yu-hong, HU Guang-rui, HE Xu-ming   

  1. Department of Electronic Engineering, Shanghai Jiaotong University 200030, China
  • Received:2001-04-14 Revised:2001-06-28 Online:2002-06-30 Published:2002-06-30

PDF

139

被引次数

8

摘要： 提出了基于进化高斯混合模型(EGMM)的说话人辨认系统建模方法.EGMM在进化算法的框架下,为改善模型的泛化性能对GMM模型的结构与参数共同进行了优化.同时,系统的优化目标中引入了其他用户的区分性信息以提高其分类精度.根据GMM的特点设计了专门的遗传算子并结合GA与EP提出了一种新的混合进化算法.初步实验结果表明,EGMM方法建立的说话人模型具有更强的泛化能力.在说话人辨认实验中,较之传统的GMM方法,基于EGMM的系统的正识率提高了近3%,并且模型具有更小的平均尺寸.

关键词: 说话人识别, 模型选择, 进化算法

Abstract: When training data is limited, the performances of speaker identification (SI) system deteriorate owing to the weak generalization of GMM trained by EM algorithm. This paper presents a solution of this problem by using a evolutionary Gaussian mixture model (EGMM) as a modeling method of SI system. Based on evolutionary algorithm, EGMM optimizes both the structure and the parameters of a GMM to better its generalization ability. Also, other speakers' discriminative information is integrated into objective function to increase the accuracy of classification. According to the characteristics of GMM, we design two special evolutionary operators and present a new hybrid evolutionary algorithm using genetic algorithms and evolutionary programming. The preliminary experimental results show that the speaker models based on EGMM have more generalization ability. Compared with traditional GMM, the correct recognition rate of SI system based on EGMM increases by approximate 3%. Furthermore, the GMMS in new system have smaller average size.

Key words: evolutionary algorithm, speaker identification, model selection

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