Speech Recognition Using Dynamic Programming of Bayesian Neural Networks

1998 ◽  
pp. 71-76
Author(s):  
Chaug-Ching Huang ◽  
Jhing-Fa Wang ◽  
Chung-Hsien Wu ◽  
Jau-Yien Lee
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Speech Recognition ◽  
Bayesian Neural Networks

HUMAN-LIKE DYNAMIC PROGRAMMING NEURAL NETWORKS FOR DYNAMIC TIME WARPING SPEECH RECOGNITION

1995 ◽  
Vol 06 (01) ◽  
pp. 79-89 ◽  
Author(s):  
CHINCHUAN CHIU ◽  
MICHAEL A. SHANBLATT
Keyword(s):  
Neural Networks ◽  
Dynamic Programming ◽  
Speech Recognition ◽  
Dynamic Time Warping ◽  
Hardware Implementation ◽  
Time Warping ◽  
Network Method ◽  
The Neural Networks ◽  
Dynamic Time ◽  
Better Than

This paper presents a human-like dynamic programming neural network method for speech recognition using dynamic time warping. The networks are configured, much like human’s, such that the minimum states of the network’s energy function represent the near-best correlation between test and reference patterns. The dynamics and properties of the neural networks are analytically explained. Simulations for classifying speaker-dependent isolated words, consisting of 0 to 9 and A to Z, show that the method is better than conventional methods. The hardware implementation of this method is also presented.

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