Title :
A study on the recognition of the isolated digits using recurrent neural predictive HMM
Author :
Kim, Soo-Hoon ; Koh, Si-Young ; Ahn, Jeom-Young ; Hur, Kang-In
Author_Institution :
Dept. of Electron. Eng., Dong-A Univ., Pusan, South Korea
Abstract :
We composed the recurrent neural predictive HMM (RNPHMM) to provide the dynamic feature of the speech pattern for HMM. The RNPHMM is the hybrid network of the recurrent neural network and the HMM. In the experiment, we compared the recognition abilities of the RNPHMM as we increased the state number, prediction order, and number of hidden nodes for the isolated digits. The models of the recurrent neural predictive HMM are the Elman network prediction HMM and the Jordan network prediction HMM. As a result of the experiments, the Elman network prediction HMM and the Jordan network prediction HMM have a good recognition ability of 98.5% for test data respectively
Keywords :
feature extraction; hidden Markov models; prediction theory; recurrent neural nets; speech recognition; Elman network prediction HMM; Jordan network prediction HMM; dynamic feature; experiment; experiments; hidden Markov model; hidden nodes; hybrid network; isolated digits recognition; prediction order; recurrent neural network; recurrent neural predictive HMM; speech pattern; speech recognition; test data; Covariance matrix; Hidden Markov models; Neural networks; Pattern recognition; Predictive models; Probability; Recurrent neural networks; Speech recognition; Statistics; Testing;
Conference_Titel :
TENCON 99. Proceedings of the IEEE Region 10 Conference
Conference_Location :
Cheju Island
Print_ISBN :
0-7803-5739-6
DOI :
10.1109/TENCON.1999.818484
