Wednesday 24th of April 2024
 

Model based neuro-fuzzy ASR on Texas processor


Hesam Ekhtiyar, Mehdi Sheida and Somaye Sobati Moghadam

In this paper an algorithm for recognizing speech has been proposed. The recognized speech is used to execute related commands which use the MFCC and two kind of classifiers, first one uses MLP and second one uses fuzzy inference system as a classifier. The experimental results demonstrate the high gain and efficiency of the proposed algorithm. We have implemented this system based on graphical design and tested on a fix point digital signal processor (DSP) of 600 MHz, with reference DM6437-EVM of Texas instrument.

Keywords: fix point DSP, model base design, Neuro-Fuzzy network, Speech recognition

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ABOUT THE AUTHORS

Hesam Ekhtiyar
Hesam Ekhtiyar received the B.S. degree in computer engineering from Hakim Sabzevari University, Sabzevar, Iran, in 2011. his research interests include computer vision, speech recognition, robotics, soft computing

Mehdi Sheida
Mahdi Sheida received the B.S. degree in computer engineering from Hakim Sabzevari University, Sabzevar, Iran, in 2011. his research interests include computer vision, speech recognition, network programming.

Somaye Sobati Moghadam
Somayeh Sobati Moghadam is lecturer in Hakim Sabzevari University. She received her B.Sc. degree in Applied Mathematics in computer in 2001 from Amir Kabir University of technology and M.Sc. degree in IT in 2007 from INSA-Lyon- France Univercity. From 2010 she is lecturer in Hakim Sabzevari University.Her research interests include Computer Vision, GIS and Security


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