Proceedings of the 12th International Conference on Green Technology (ICGT 2022)

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Human Voice Recognition System with Backpropagation Neural Network Method

Authors
Mohammad Bagus Dimas Prayugo1, *, Nanda Azzahrotun Nafisa1, Azis Yulianas1, Hisyam Fahmi1
1Mathematic Department, Faculty of Science and Technology, Universitas Islam Negeri Maulana Malik Ibrahim Malang, Malang, Indonesia
*Corresponding author. Email: 19610001@student.uin-malang.ac.id
Corresponding Author
Mohammad Bagus Dimas Prayugo
Available Online 29 May 2023.
DOI
10.2991/978-94-6463-148-7_43How to use a DOI?
Keywords
Artificial Neural Network; Backpropagation; Voice; Mean Square Error (MSE)
Abstract

The system on the computer can make everything run quickly and efficiently, so that it becomes a tool in information processing. One of the computer systems is an Artificial Neural Network (ANN). Along with technological advances, events that require computational models to perform speech recognition can be useful for science, as well as for making practical applications such as voice-based security systems. Artificial neural network is a method of grouping and separating data that has a working system like a neural network in humans. Artificial neural networks can pick up patterns that have been perfectly studied and well received. Backpropagation is a systematic method for training multiple layers of artificial neural networks. The backpropagation network model is composed of an input layer, at least one hidden layer and an output layer. Voice data in the form of signals is converted into discrete data by LPC and FFT methods. The activation function used is the sigmoid function, 2 hidden layers and the number of neurons 15. Optimal training was obtained in the 4th experiment with an MSE error of 0.19413 with a time of 11 s with 678 iterations. System accuracy to training data is 90%, and accuracy to test data is 40%. This means that the level of system accuracy can run well.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 12th International Conference on Green Technology (ICGT 2022)
Series
Advances in Engineering Research
Publication Date
29 May 2023
ISBN
10.2991/978-94-6463-148-7_43
ISSN
2352-5401
DOI
10.2991/978-94-6463-148-7_43How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Mohammad Bagus Dimas Prayugo
AU  - Nanda Azzahrotun Nafisa
AU  - Azis Yulianas
AU  - Hisyam Fahmi
PY  - 2023
DA  - 2023/05/29
TI  - Human Voice Recognition System with Backpropagation Neural Network Method
BT  - Proceedings of the 12th International Conference on Green Technology (ICGT 2022)
PB  - Atlantis Press
SP  - 432
EP  - 442
SN  - 2352-5401
UR  - https://doi.org/10.2991/978-94-6463-148-7_43
DO  - 10.2991/978-94-6463-148-7_43
ID  - Prayugo2023
ER  -