Yulianto, Haekal Rizky and Afiahayati, Afiahayati (2021) Fighting COVID-19: Convolutional Neural Network for Elevator User's Speech Classification in Bahasa Indonesia. In: Procedia Computer Science.
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The use of deep learning to automatically recognize and classify the floor numbers and commands spoken by elevator users could help reduce transmission of COVID-19 by physical contact with the elevator button. Fortunately, the ability of Convolutional Neural Network (CNN) as one of the deep learning architectures to recognize patterns is well-known. This research aims to create isolated word data of spoken floor numbers and commands, then build a classifier model to recognize and classify floor numbers and commands spoken by elevator users. In this research, speech data are gathered in Bahasa Indonesia and classified using CNN and Multilayer Perceptron (MLP). At the end of this research, it is found that 94 of classification accuracy is provided by the best CNN model configuration towards test data. This outcome is better than the MLP model which provides 80 of accuracy. © 2021 Elsevier B.V.. All rights reserved.
Item Type: | Conference or Workshop Item (Paper) |
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Additional Information: | Cited by: 6; All Open Access, Gold Open Access |
Uncontrolled Keywords: | Convolution; Convolutional neural networks; Deep neural networks; Elevators; Floors; Convolutional neural network; COVID-19; Deep learning; Elsevier; Indonesia; Isolated words; Learning architectures; Multilayers perceptrons; Physical contacts; Speech classification; Classification (of information) |
Subjects: | Q Science > QA Mathematics Q Science > QA Mathematics > QA75 Electronic computers. Computer science |
Divisions: | Faculty of Mathematics and Natural Sciences > Computer Science & Electronics Department |
Depositing User: | Sri JUNANDI |
Date Deposited: | 06 Oct 2024 09:14 |
Last Modified: | 06 Oct 2024 09:14 |
URI: | https://ir.lib.ugm.ac.id/id/eprint/8761 |