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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2016-01-01
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Series: | MATEC Web of Conferences |
Online Access: | http://dx.doi.org/10.1051/matecconf/20167507001 |
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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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