Tunable time delay neural networks for isolated word recognition

Author

Wu, Duanpei ; Gowdy, John N.

Author_Institution

Dept. of Electr. & Comput. Eng., Clemson Univ., SC, USA

fYear

1994

fDate

13-16 Apr 1994

Firstpage

105

Abstract

In this article, we describe a new neural network structure and a corresponding new sequential training technique for speech recognition. The proposed system is a modification of the original time delay neural network (TDNN) structure of Waibel (1989). The new structure consists of a group of sub-nets, and each isolated word to be recognized corresponds to at least one sub-net. Since each sub-net deals with only one word, it may be trained independently. Each sub-net is a TDNN which we train with a new sequential training algorithm. The system has attained close to 100% accuracy for a two-speaker, isolated word recognition task

Keywords

delays; learning (artificial intelligence); speech recognition; tuning; TDNN; accuracy; isolated word recognition; neural network structure; sequential training algorithm; sub-nets; tunable time delay neural networks; two-speaker recognition; Artificial neural networks; Degradation; Delay effects; Hidden Markov models; Image processing; Image recognition; Neural networks; Speech processing; Speech recognition; System performance;

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.1994.344954

Filename

344954