Construction of a Bi-Modal Database for a Barrier-Free Teaching System

2019 ◽  
Vol 11 (3) ◽  
pp. 64-78
Author(s):  
Jiling Tang ◽  
Ping Feng ◽  
Zhanlei Li
Keyword(s):  
Experimental Data ◽  
Speech Recognition ◽  
Education System ◽  
Recognition System ◽  
Deaf Students ◽  
Teaching System ◽  
Speech Recognition Technology ◽  
Barrier Free

This article analyzes the application of Chinese speech recognition technology in a non-barrier education system, and studies the construction of bi-modal database for a barrier-free teaching system. Barrier-free teaching is a term used to define systems that are designed to assist deaf students, while bi-modal approaches use both audio and video to assist learning. Based on a case study of a curriculum named “Foundations of Photoshop,” the article creates a corpus to make acquisition of experimental data and annotation of corpora. Meanwhile, the authors analyze and design the organization of data and build an essential dictionary and grammar network in the recognition system.

Use of Posterior Probabilities to Evaluate Methods of Discriminant Analysis

1981 ◽  
Vol 20 (04) ◽  
pp. 207-212 ◽  
Author(s):  
J. Hermans ◽  
B. van Zomeren ◽  
J. W. Raatgever ◽  
P. J. Sterk ◽  
J. D. F. Habbema
Keyword(s):  
Experimental Data ◽  
Discriminant Analysis ◽  
Posterior Probabilities

By means of a case study the choice between several methods of discriminant analysis is presented. Experimental data of a two-groups problem with one or two variables is analysed. The different methods are compared according to posterior probabilities which can be computed for each subject and which are the basis of discriminant analysis. These posterior probabilities are analysed graphically as well as numerically.

Soft Skills among Students: A Case Study of UiTM Cawangan Kelantan

2019 ◽  
Vol 5 (1) ◽  
pp. 1
Author(s):  
Muhammad Faizal Samat ◽  
Norazlan Annual ◽  
Raznee Atisya Md Rashidi
Keyword(s):  
Problem Solving ◽  
Unemployment Rate ◽  
Significant Influence ◽  
Education System ◽  
Communication Skill ◽  
Soft Skills ◽  
Team Building ◽  
Leadership Skill ◽  
Quality In Education

This article contributes to ongoing debates about soft skills among students. In 2017, the unemployment rate in Malaysia was at 3.42 percent as compared to 2.85 percent in 2014. Education system must aim towards employability and ensure quality in education to reduce the percentage of unemployment. Thus, this study aims to investigate the development of soft skills among students through co-curriculum activities in UiTM Cawangan Kelantan. The sample were 113 students from UiTM Cawangan Kelantan. Questionnaires adapted from previous research to measure the communication skill, problem solving skill, team building skill, leadership skill and soft development of soft skills among students through co-curriculum activities. SEM-PLS 3.0 were employed in this study. The findings revealed only team building skill has significant influence on developments of soft skills among students through co-curriculum activities. However, the study indicates that communication skill, problem solving skill and leadership skill are not significant towards development of soft skills among students through cocurriculum activities.

Design Of A Voice Controlled Home Automation System Using Deep Learning Convolutional Neural Network (DL-CNN)

2020 ◽  
Vol 8 (1) ◽  
pp. 57-73
Author(s):  
Lery Sakti Ramba
Keyword(s):  
Deep Learning ◽  
Speech Recognition ◽  
Background Noise ◽  
Electronic Devices ◽  
Recognition System ◽  
Background Intensity ◽  
Automation System ◽  
Home Automation ◽  
Speech Recognition System ◽  
Home Automation System

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.

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