With the advancement of technology, the demand for improving the quality of life of the elderly and disabled has increased and their hope to overcome their problem is realized by using advanced technologies in the field of rehabilitation. Many existing electrical and electronic devices can be turned into more controllable and more functional devices using artificial intelligence. In every society, some spinal disabled people lack physical and motor abilities such as moving their limbs and they cannot use the normal wheelchair and need a wheelchair with voice control. The main challenge of this project is to identify the voice patterns of disabled people. Audio classification is one of the challenges in the field of pattern recognition. In this paper, a method of classifying ambient sounds based on the sound spectrogram, using deep neural networks is presented to classify Persian speakers sound for building a voice-controlled intelligent wheelchair. To do this, we used Inception-V3 as a convolutional neural network which is pretrained by the ImageNet dataset. In the next step, we trained the network with images that are generated using spectrogram images of the ambient sound of about 50 Persian speakers. The experimental results achieved a mean accuracy of 83.33%. In this plan, there is the ability to control the wheelchair by a third party (such as spouse, children or parents) by installing an application on their mobile phones. This wheelchair will be able to execute five commands such as stop, left, right, front and back.
| Published in | Machine Learning Research (Volume 6, Issue 1) |
| DOI | 10.11648/j.mlr.20210601.11 |
| Page(s) | 1-7 |
| Creative Commons |
This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited. |
| Copyright |
Copyright © The Author(s), 2024. Published by Science Publishing Group |
Voice Recognition, Deep Learning, Convolutional Neural Network, Spectrogram, Inception-V3
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APA Style
Mohammad Amiri, Manizheh Ranjbar, Mostafa Azami Gharetappeh. (2021). Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset. Machine Learning Research, 6(1), 1-7. https://doi.org/10.11648/j.mlr.20210601.11
ACS Style
Mohammad Amiri; Manizheh Ranjbar; Mostafa Azami Gharetappeh. Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset. Mach. Learn. Res. 2021, 6(1), 1-7. doi: 10.11648/j.mlr.20210601.11
AMA Style
Mohammad Amiri, Manizheh Ranjbar, Mostafa Azami Gharetappeh. Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset. Mach Learn Res. 2021;6(1):1-7. doi: 10.11648/j.mlr.20210601.11
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Download@article{10.11648/j.mlr.20210601.11,
author = {Mohammad Amiri and Manizheh Ranjbar and Mostafa Azami Gharetappeh},
title = {Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset},
journal = {Machine Learning Research},
volume = {6},
number = {1},
pages = {1-7},
doi = {10.11648/j.mlr.20210601.11},
url = {https://doi.org/10.11648/j.mlr.20210601.11},
eprint = {https://article.sciencepublishinggroup.com/pdf/10.11648.j.mlr.20210601.11},
abstract = {With the advancement of technology, the demand for improving the quality of life of the elderly and disabled has increased and their hope to overcome their problem is realized by using advanced technologies in the field of rehabilitation. Many existing electrical and electronic devices can be turned into more controllable and more functional devices using artificial intelligence. In every society, some spinal disabled people lack physical and motor abilities such as moving their limbs and they cannot use the normal wheelchair and need a wheelchair with voice control. The main challenge of this project is to identify the voice patterns of disabled people. Audio classification is one of the challenges in the field of pattern recognition. In this paper, a method of classifying ambient sounds based on the sound spectrogram, using deep neural networks is presented to classify Persian speakers sound for building a voice-controlled intelligent wheelchair. To do this, we used Inception-V3 as a convolutional neural network which is pretrained by the ImageNet dataset. In the next step, we trained the network with images that are generated using spectrogram images of the ambient sound of about 50 Persian speakers. The experimental results achieved a mean accuracy of 83.33%. In this plan, there is the ability to control the wheelchair by a third party (such as spouse, children or parents) by installing an application on their mobile phones. This wheelchair will be able to execute five commands such as stop, left, right, front and back.},
year = {2021}
}
TY - JOUR T1 - Designing a Voice-controlled Wheelchair for Persian-speaking Users Using Deep Learning Networks with a Small Dataset AU - Mohammad Amiri AU - Manizheh Ranjbar AU - Mostafa Azami Gharetappeh Y1 - 2021/11/05 PY - 2021 N1 - https://doi.org/10.11648/j.mlr.20210601.11 DO - 10.11648/j.mlr.20210601.11 T2 - Machine Learning Research JF - Machine Learning Research JO - Machine Learning Research SP - 1 EP - 7 PB - Science Publishing Group SN - 2637-5680 UR - https://doi.org/10.11648/j.mlr.20210601.11 AB - With the advancement of technology, the demand for improving the quality of life of the elderly and disabled has increased and their hope to overcome their problem is realized by using advanced technologies in the field of rehabilitation. Many existing electrical and electronic devices can be turned into more controllable and more functional devices using artificial intelligence. In every society, some spinal disabled people lack physical and motor abilities such as moving their limbs and they cannot use the normal wheelchair and need a wheelchair with voice control. The main challenge of this project is to identify the voice patterns of disabled people. Audio classification is one of the challenges in the field of pattern recognition. In this paper, a method of classifying ambient sounds based on the sound spectrogram, using deep neural networks is presented to classify Persian speakers sound for building a voice-controlled intelligent wheelchair. To do this, we used Inception-V3 as a convolutional neural network which is pretrained by the ImageNet dataset. In the next step, we trained the network with images that are generated using spectrogram images of the ambient sound of about 50 Persian speakers. The experimental results achieved a mean accuracy of 83.33%. In this plan, there is the ability to control the wheelchair by a third party (such as spouse, children or parents) by installing an application on their mobile phones. This wheelchair will be able to execute five commands such as stop, left, right, front and back. VL - 6 IS - 1 ER -
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