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
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;
Conference_Titel :
Neural Networks, 2001. Proceedings. IJCNN '01. International Joint Conference on
Conference_Location :
Washington, DC
Print_ISBN :
0-7803-7044-9
DOI :
10.1109/IJCNN.2001.939591
