Evolution of Speech Recognizer Agents by Artificial Life

被引：0

作者：

Halavati, Ramin ^{[1
]}

Shouraki, Saeed Bagheri ^{[1
]}

Zadeh, Saman Harati ^{[1
]}

Lucas, Caro

机构：

[1] Sharif Univ Technol, Dept Comp Engn, Artificial Creatures Lab, Tehran, Iran

来源：

PROCEEDINGS OF WORLD ACADEMY OF SCIENCE, ENGINEERING AND TECHNOLOGY, VOL 6 | 2005年

关键词：

Artificial Life; Speech Recognition; Fuzzy Modeling;

D O I：

暂无

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Artificial Life can be used as an agent training approach in large state spaces. This paper presents an artificial life method to increase the training speed of some speech recognizer agents which where previously trained by genetic algorithms. Using this approach, vertical training (genetic mutations and selection) is combined with horizontal training (individual learning through reinforcement learning) and results in a much faster evolution than simple genetic algorithm. The approach is tested and a comparison with GA cases on a standard speech data base is presented.

引用

页码：237 / 240

页数：4