Integrating neural nets and one-stage dynamic programming for speaker independent continuous Mandarin digit recognition

Author(s):  
Jhing-Fa Wang ◽  
Chung-Hsien Wu ◽  
Chaug-Ching Haung ◽  
Jau-Yien Lee
1977 ◽  
Vol 14 (4) ◽  
pp. 795-805 ◽  
Author(s):  
Ernst–Erich Doberkat

A dynamic programming approach for the investigation of learning systems is taken. Making use of one-stage decision models and dynamic programs, respectively, two learning models are formulated and the existence of optimal strategies for learning in the respective models is proven.


1992 ◽  
Vol 4 (1) ◽  
pp. 108-119 ◽  
Author(s):  
K. P. Unnikrishnan ◽  
J. J. Hopfield ◽  
D. W. Tank

The capability of a small neural network to perform speaker-independent recognition of spoken digits in connected speech has been investigated. The network uses time delays to organize rapidly changing outputs of symbol detectors over the time scale of a word. The network is data driven and unclocked. To achieve useful accuracy in a speaker-independent setting, many new ideas and procedures were developed. These include improving the feature detectors, self-recognition of word ends, reduction in network size, and dividing speakers into natural classes. Quantitative experiments based on Texas Instruments (TI) digit databases are described.


Sign in / Sign up

Export Citation Format

Share Document