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Speech Recognition using Neural Network

Published on August 2015 by Pankaj Rani, Sushil Kakkar, Shweta Rani
International Conference on Advancements in Engineering and Technology
Foundation of Computer Science USA
ICAET2015 - Number 4
August 2015
Authors: Pankaj Rani, Sushil Kakkar, Shweta Rani
0c800ff0-b408-4307-a9e4-f59ad75db926

Pankaj Rani, Sushil Kakkar, Shweta Rani . Speech Recognition using Neural Network. International Conference on Advancements in Engineering and Technology. ICAET2015, 4 (August 2015), 11-14.

@article{
author = { Pankaj Rani, Sushil Kakkar, Shweta Rani },
title = { Speech Recognition using Neural Network },
journal = { International Conference on Advancements in Engineering and Technology },
issue_date = { August 2015 },
volume = { ICAET2015 },
number = { 4 },
month = { August },
year = { 2015 },
issn = 0975-8887,
pages = { 11-14 },
numpages = 4,
url = { /proceedings/icaet2015/number4/22229-4052/ },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Proceeding Article
%1 International Conference on Advancements in Engineering and Technology
%A Pankaj Rani
%A Sushil Kakkar
%A Shweta Rani
%T Speech Recognition using Neural Network
%J International Conference on Advancements in Engineering and Technology
%@ 0975-8887
%V ICAET2015
%N 4
%P 11-14
%D 2015
%I International Journal of Computer Applications
Abstract

Speech recognition is a subjective phenomenon. Despite being a huge research in this field, this process still faces a lot of problem. Different techniques are used for different purposes. This paper gives an overview of speech recognition process. Various progresses have been done in this field. In this work of project, it is shown that how the speech signals are recognized using back propagation algorithm in neural network. Voices of different persons of various ages in a silent and noise free environment by a good quality microphone are recorded. Same sentence of duration 10-12 seconds is spoken by these persons. These spoken sentences are then converted into wave formats. Then features of the recorded samples are extracted by training these signals using LPC. Learning is required whenever we don't have the complete information about the input or output signal. At the input stage, 128 samples of each sentence are applied, then through hidden layers these are passed to output layer. These networks are trained to perform tasks such as pattern recognition, decision making and motoric control.

References
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Index Terms

Computer Science
Information Sciences

Keywords

Neural Network Speech Recognition Back Propagation Training Algorithm.