Speech recognition by extended loop neural network

Author

Zhenjiang, Miao ; Baozong, Yuan

Author_Institution

Inst. of Inf. Sci., Northern Jiaotong Univ., Beijing, China

fYear

1994

fDate

13-16 Apr 1994

Firstpage

335

Abstract

Presents an extended loop neural network approach to speech recognition. This speech recognition approach is characterized by the following important properties due to the associative memory neural network. (1) It has the features of great adaptivity and fault tolerance to carry out recognition. (2) The recognition system can be constructed which allows for the formation of arbitrary nonlinear decision surfaces. (3) The recognition system can perform not only the recognition task but also restore the correct information from incomplete even some extent incorrect information at the same time. Experiments are also conducted and the results show that this speech recognition approach has great application potentials

Keywords

content-addressable storage; fault tolerant computing; neural nets; speech recognition; arbitrary nonlinear decision surfaces; associative memory neural network; correct information; extended loop neural network; fault tolerance; speech recognition; Associative memory; Character recognition; Hidden Markov models; Hopfield neural networks; Information science; Multi-layer neural network; Neural networks; Pattern recognition; Recurrent neural networks; Speech recognition;

fLanguage

English

Publisher

ieee

Conference_Titel

Speech, Image Processing and Neural Networks, 1994. Proceedings, ISSIPNN '94., 1994 International Symposium on

Print_ISBN

0-7803-1865-X

Type

conf

DOI

10.1109/SIPNN.1994.344898

Filename

344898