scholarly journals ECHO: Hands-Free Computer Interaction using Speech Recognition System for the Debilitated

2019 ◽  
Vol 8 (2) ◽  
pp. 1768-1772

Computer nowadays is a must-have tool for most people. However, it is not a tool to be used by people with physical disabilities, especially the ones lacking an arm or two. The goal of this paper is to introduce a system that will help computer users perform tasks and make use of computer features and functions despite their physical limitations through the Speech Recognition System (SRS) in the English language. Ideally, this aims to provide users with an alternative way of interacting with the computer system and navigate through its functions using SRS in place of peripheral devices. It can be used to navigate through menus, open and manage applications, open certain websites, browse the internet, and type words, letters, numbers, and symbols using the dictation mode. For the testing phase, the following test cases were used: Functionality Testing, Stress Testing, and Compatibility. The testing phase yielded a result of 94.79% for the functional, 100% for stress, and 100% for compatibility, effectively ensuring that the software is working as intended. The evaluation results conforming to the standards of the ISO/IEC 9126-1 yielded a mean of 3.57 with a standard deviation of 0.52 interpreted as ‘Highly Acceptable', which means that the software can be used as an effective alternative to peripheral devices and can even be used to complement its usage.

Author(s):  
Lery Sakti Ramba

The purpose of this research is to design home automation system that can be controlled using voice commands. This research was conducted by studying other research related to the topics in this research, discussing with competent parties, designing systems, testing systems, and conducting analyzes based on tests that have been done. In this research voice recognition system was designed using Deep Learning Convolutional Neural Networks (DL-CNN). The CNN model that has been designed will then be trained to recognize several kinds of voice commands. The result of this research is a speech recognition system that can be used to control several electronic devices connected to the system. The speech recognition system in this research has a 100% success rate in room conditions with background intensity of 24dB (silent), 67.67% in room conditions with 42dB background noise intensity, and only 51.67% in room conditions with background intensity noise 52dB (noisy). The percentage of the success of the speech recognition system in this research is strongly influenced by the intensity of background noise in a room. Therefore, to obtain optimal results, the speech recognition system in this research is more suitable for use in rooms with low intensity background noise.


Author(s):  
Shansong Liu ◽  
Shoukang Hu ◽  
Xurong Xie ◽  
Mengzhe Geng ◽  
Mingyu Cui ◽  
...  

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