scholarly journals Investigation of Automatic Speech Recognition Systems via the Multilingual Deep Neural Network Modeling Methods for a Very Low-Resource Language, Chaha

2020 ◽  
Vol 11 (01) ◽  
pp. 1-21
Author(s):  
Tessfu Geteye Fantaye ◽  
Junqing Yu ◽  
Tulu Tilahun Hailu
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Automatic Speech Recognition ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
Low Resource ◽  
Modeling Methods ◽  
Recognition Systems

A study on global optimization and deep neural network modeling method in performance-seeking control

2019 ◽  
Vol 234 (1) ◽  
pp. 46-59 ◽  
Author(s):  
Qiangang Zheng ◽  
Dawei Fu ◽  
Yong Wang ◽  
Haoying Chen ◽  
Haibo Zhang
Keyword(s):  
Neural Network ◽  
Genetic Algorithm ◽  
Global Optimization ◽  
Interval Analysis ◽  
Sequential Quadratic Programming ◽  
Deep Neural Network ◽  
Network Modeling ◽  
Engine Performance ◽  
Neural Network Modeling ◽  
Swarm Optimization

In this article, a novel performance-seeking control method based on deep neural network and interval analysis is proposed to obtain a better engine performance. A deep neural network modeling method which has stronger representation capability than conventional neural network and can deal with big training data is adopted to establish an on-board model in the subsonic and supersonic cruising envelops. Meanwhile, a global optimization algorithm interval analysis is applied here to get a better engine performance. Finally, two simulation experiments are conducted to verify the effectiveness of the proposed methods. One is the on-board model modeling which compares the deep neural network with the conventional neural network, and the other is the performance-seeking control simulations comparing interval analysis with feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm, respectively. These two experiments show that the deep neural network has much higher precision than the conventional neural network and the interval analysis gets much better engine performance than feasible sequential quadratic programming, particle swarm optimization, and genetic algorithm.

End-to-end neural network modeling for Japanese speech recognition

2016 ◽  
Vol 140 (4) ◽  
pp. 3116-3116
Author(s):  
Hitoshi Ito ◽  
Aiko Hagiwara ◽  
Manon Ichiki ◽  
Takeshi Mishima ◽  
Shoei Sato ◽  
...  
Keyword(s):  
Neural Network ◽  
Speech Recognition ◽  
Network Modeling ◽  
Neural Network Modeling ◽  
End To End
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