A speech recognition method based clustering neural network integration

Author

Zhang, Jing ; Zhang, Min

Author_Institution

Dept. of Comput. Sci. & Technol., Guangdong Univ. of Foreign Studies, Guangzhou, China

fYear

2011

fDate

15-17 April 2011

Firstpage

1120

Lastpage

1122

Abstract

An improved BP neural network classifier integration method was mainly described, by which using k-means clustering a group of value of weights and thresholds with some differences were gotten, and then as the value of individuals of integrated network to improve the performance of integrated learning, and be successfully applied to non-specific human isolated word speech recognition system. By comparing the experimental result and the traditional Adaboost integration algorithm, the validity of the method was confirmed.

Keywords

backpropagation; neural nets; pattern clustering; speech recognition; Adaboost integration algorithm; BP neural network classifier integration method; backpropagation neural network; human isolated word speech recognition system; k-means clustering; speech recognition method; Artificial neural networks; Classification algorithms; Clustering algorithms; Predictive models; Speech recognition; Training; Training data; Speech Recognition; difference; integrated learning; k-means clustering;

fLanguage

English

Publisher

ieee

Conference_Titel

Electric Information and Control Engineering (ICEICE), 2011 International Conference on

Conference_Location

Wuhan

Print_ISBN

978-1-4244-8036-4

Type

conf

DOI

10.1109/ICEICE.2011.5777537

Filename

5777537