IAES International Journal of Artificial Intelligence (IJ-AI) Vol. 9, No. 3, September 2020, pp. 387~393 ISSN: 2252-8938, DOI: 10.11591/ijai.v9.i3.pp387-393  387 Journal homepage: http://ijai.iaescore.com Comparison of CNNs and SVM for voice control wheelchair Mohammad Shahrul Izham Sharifuddin, Sharifalillah Nordin, Azliza Mohd Ali Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia Article Info ABSTRACT Article history: Received Feb 10, 2020 Revised Apr 20, 2020 Accepted May 6, 2020 In this paper, we develop an intelligent wheelchair using CNNs and SVM voice recognition methods. The data is collected from Google and some of them are self-recorded. There are four types of data to be recognized which are go, left, right, and stop. Voice data are extracted using MFCC feature extraction technique. CNNs and SVM are then used to classify and recognize the voice data. The motor driver is embedded in Raspberry PI 3B+ to control the movement of the wheelchair prototype. CNNs produced higher accuracy i.e. 95.30% compared to SVM which is only 72.39%. On the other hand, SVM only took 8.21 seconds while CNNs took 250.03 seconds to execute. Therefore, CNNs produce better result because noise are filtered in the feature extraction layer before classified in the classification layer. However, CNNs took longer time due to the complexity of the networks and the less complexity implementation in SVM give shorter processing time. Keywords: Convolutional neural networks Support vector machine Voice recognition This is an open access article under the CC BY-SA license. Corresponding Author: Sharifalillah Nordin, Faculty of Computer and Mathematical Sciences, Universiti Teknologi MARA, Shah Alam, Selangor, Malaysia. Email: sharifa@fskm.uitm.edu.my 1. INTRODUCTION Impaired people is increasing every year due to ageing, accidents and also diseases such as paralysis, spinal cord injuries (SCI), amputation and quadriplegia [1]. This group of people require a special wheelchair to move such as a motorised wheelchair. Motorised wheelchair consists of electric motor and joystick at the armrest. However, disabled people with paralysed hand or having a problem with the hand cannot operate joystick on the motorised wheelchair to move. Therefore, a lot of researches studied to help the impaired people move the wheelchair-using voice recognition [1-3]. This wheelchair can be called smart or intelligent wheelchair because it can be moved using only the voice. The first intelligent wheelchair was developed by the Siamo University of Alcala, Spain in 1999 [4]. The improvement of voice and speech identification technology has been started by Texas Instruments in 1960 [5]. Recently, voice recognition or speech recognition has been applied in assisting people doing work through digital devices such as mobile phone, tablets, and personal computer. There are also voice recognition software and webpages i.e. google applications, translation software, and personal assistants such as Alexa, Cortana and Siri. This personal assistant can be called modern chatbot which can assist people in any topics they would like to ask. The advancement of technology in artificial intelligence (AI) have created a smart wheelchair or intelligent wheelchair exploiting the voice recognition features. A lot of researches have been conducted to improve the functionality of the wheelchair. Nasrin [1] proposed an application on smart wheelchair-using voice and add GPS function to track the user navigation and location. This application required WIFI connection and the application is installed in a mobile environment. However, Avutu et. al. [3] proposed a