Persian Vowel Recognition Using the Combination of Haar Wavelet and Neural Network

2013 ◽  
pp. 53-68 ◽  
Author(s):  
Mohammad Mehdi Hosseini ◽  
Abdorreza Alavi Gharahbagh
Keyword(s):  
Neural Network ◽  
Haar Wavelet ◽  
Vowel Recognition

Comparing performance of spectral distance measures and neural network methods for vowel recognition

1989 ◽  
Vol 3 (1) ◽  
pp. 21-34 ◽  
Author(s):  
Candace A. Kamm ◽  
Lynn A. Streeter ◽  
Yana Kane-Esrig ◽  
David J. Burr
Keyword(s):  
Neural Network ◽  
Distance Measures ◽  
Spectral Distance ◽  
Network Methods ◽  
Vowel Recognition

Vowel recognition with a time-delay neural network

2002 ◽  
Author(s):  
R.A. Mitchell ◽  
A. Shaw
Keyword(s):  
Neural Network ◽  
Time Delay ◽  
Vowel Recognition

scholarly journals On-chip Reconfigurable Optical Neural Networks

2021 ◽  
Author(s):  
Xianmeng Zhao ◽  
Haibin Lv ◽  
Cheng Chen ◽  
Shenjie Tang ◽  
Xiaoping Liu ◽  
...  
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Optical Networks ◽  
Full Adder ◽  
Silicon On Insulator ◽  
Neuron Networks ◽  
Control Electronics ◽  
Optical Neural Network ◽  
Vowel Recognition ◽  
On Chip

Abstract Implementing artificial neural networks on integrated platforms has generated significant interest in recent years. Several architectures for on-chip optical networks with basic functionalities have been successfully demonstrated, for example, optical spiking neurosynaptic, photonic convolution accelerator, and nanophotonic/electronic hybrid deep neuron networks. In this work, we propose a layered coherent silicon-on-insulator diffractive optical neural network, of which the inter-layer phase delay can be actively tuned. By forming a close-loop with control electronics, we further demonstrate that our fabricated on-chip neural network can be trained in-situ and consequently reconfigured to perform various tasks, including full adder operation and vowel recognition, while achieving almost the same accuracy as networks trained on conventional computers. Our results show that the proposed optical neural network could potentially pave the way for future optical artificial intelligence hardware.

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