DocumentCode
1749278
Title
A high performance neural-networks-based speech recognition system
Author
Yang, Song ; Er, Meng Joo ; Gao, Yang
Author_Institution
Sch. of Electr. & Electron. Eng., Nanyang Technol. Univ., Singapore
Volume
2
fYear
2001
fDate
2001
Firstpage
1527
Abstract
A high performance neural-network-based speech recognition system is presented. A new approach towards feature representation for speech recognition, named state transition matrix (STM), is proposed to address temporal varying problem in speech recognition. Using STM, we need only a single-layer perceptron neural network to perform speech recognition. Experimental results show that an overall accuracy of 95% and 87% was achieved for speaker-dependent isolated word recognition and multi-speaker-dependent isolated word recognition, respectively
Keywords
backpropagation; feature extraction; neural nets; speech recognition; backpropagation; feature extraction; neural-network; single-layer perceptron; speech recognition; state transition matrix; temporal varying problem; Backpropagation algorithms; Erbium; Hidden Markov models; Humans; Network topology; Neural networks; Paper technology; Speech processing; Speech recognition; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location
Washington, DC
ISSN
1098-7576
Print_ISBN
0-7803-7044-9
Type
conf
DOI
10.1109/IJCNN.2001.939591
Filename
939591
Link To Document