Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network

Backpropagation Artificial Neural Network (ANN) is a well known branch of Artificial Intelligence and has been proven to solve various problems of complex speech recognizing in health [1], [2], education [4] and engineering [3]. Today, many kinds of presentation tools are used by society. One popula...

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Main Authors: Hasanah Nur, Irmawati Dessy, Arifin Fatchul, Marpanaji Eko
Format: Article
Language:English
Published: EDP Sciences 2016-01-01
Series:MATEC Web of Conferences
Online Access:http://dx.doi.org/10.1051/matecconf/20167507001
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spelling doaj-de4493e56e334b34b2a4e9ecf83cebf22021-04-02T05:18:19ZengEDP SciencesMATEC Web of Conferences2261-236X2016-01-01750700110.1051/matecconf/20167507001matecconf_icmie2016_07001Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural NetworkHasanah NurIrmawati DessyArifin FatchulMarpanaji EkoBackpropagation Artificial Neural Network (ANN) is a well known branch of Artificial Intelligence and has been proven to solve various problems of complex speech recognizing in health [1], [2], education [4] and engineering [3]. Today, many kinds of presentation tools are used by society. One popular example is MsPowerpoint. The transition process between slides in presentation tools will be more easily done through speech, the sound emitted directly by the user during the presentation. This study uses research and development to create a simulation using Backpropagation ANN for speech recognition from number one to five to navigate slides of the presentation tool. The Backpropagation ANN consists of one input layer, one hidden layer with 100 neurons and one output layer. The simulation is built by using a Neural Network Toolbox Matlab R2014a. Speech samples were taken from five different people with wav format. This research shows that the Backpropagation ANN can be used as navigation through speech with 96% accuracy rate based on the network training result. Thesimulation can produce 63% accuracy based on 100 new speech samples from various sources.http://dx.doi.org/10.1051/matecconf/20167507001
collection DOAJ
language English
format Article
sources DOAJ
author Hasanah Nur
Irmawati Dessy
Arifin Fatchul
Marpanaji Eko
spellingShingle Hasanah Nur
Irmawati Dessy
Arifin Fatchul
Marpanaji Eko
Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network
MATEC Web of Conferences
author_facet Hasanah Nur
Irmawati Dessy
Arifin Fatchul
Marpanaji Eko
author_sort Hasanah Nur
title Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network
title_short Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network
title_full Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network
title_fullStr Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network
title_full_unstemmed Speech Recognizing for Presentation Tool Navigation Using Back Propagation Artificial Neural Network
title_sort speech recognizing for presentation tool navigation using back propagation artificial neural network
publisher EDP Sciences
series MATEC Web of Conferences
issn 2261-236X
publishDate 2016-01-01
description Backpropagation Artificial Neural Network (ANN) is a well known branch of Artificial Intelligence and has been proven to solve various problems of complex speech recognizing in health [1], [2], education [4] and engineering [3]. Today, many kinds of presentation tools are used by society. One popular example is MsPowerpoint. The transition process between slides in presentation tools will be more easily done through speech, the sound emitted directly by the user during the presentation. This study uses research and development to create a simulation using Backpropagation ANN for speech recognition from number one to five to navigate slides of the presentation tool. The Backpropagation ANN consists of one input layer, one hidden layer with 100 neurons and one output layer. The simulation is built by using a Neural Network Toolbox Matlab R2014a. Speech samples were taken from five different people with wav format. This research shows that the Backpropagation ANN can be used as navigation through speech with 96% accuracy rate based on the network training result. Thesimulation can produce 63% accuracy based on 100 new speech samples from various sources.
url http://dx.doi.org/10.1051/matecconf/20167507001
work_keys_str_mv AT hasanahnur speechrecognizingforpresentationtoolnavigationusingbackpropagationartificialneuralnetwork
AT irmawatidessy speechrecognizingforpresentationtoolnavigationusingbackpropagationartificialneuralnetwork
AT arifinfatchul speechrecognizingforpresentationtoolnavigationusingbackpropagationartificialneuralnetwork
AT marpanajieko speechrecognizingforpresentationtoolnavigationusingbackpropagationartificialneuralnetwork
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