scholarly journals Mono-font Cursive Arabic Text Recognition Using Speech Recognition System

2006 ◽  
pp. 755-763 ◽  
Author(s):  
M. S. Khorsheed
Keyword(s):  
Speech Recognition ◽  
Recognition System ◽  
Text Recognition ◽  
Arabic Text ◽  
Speech Recognition System

scholarly journals Challenges of Accent and vowels for Sindhi Speech Recognition System

2021 ◽  
Vol 10 (2) ◽  
pp. 916-921
Keyword(s):  
Artificial Intelligence ◽  
Speech Recognition ◽  
Recognition System ◽  
Text Recognition ◽  
Speech Recognition System ◽  
The World ◽  
Computer Scientists ◽  
Difficult Area ◽  
Area Of Study

While talking and writing in Sindhi language, many challenges are faced because of the large number of 52 characters or alphabets. Vowels and the accent keep changing in fluency of speaking and writing. Due to the different varities of languages in the world and the dearth of computer scientists in the field of Speech Recognition, it is considered difficult area of study and is the least advanced field of Artificial Intelligence. More specifically, the difficulties are faced in the speech recognition for languages like Arabic and its adapting languages such as Sindhi, Pashto, Urdu, and others. The script and sounds in every language are directly proportional to each other i.e. the shorter script has less sounds while the longer script has more sounds. We developed a system for speech to text recognition system for Sindhi language with the help of Sphinx model. We have also tested the different datasets through the input in various phases and compare the results and accuracy of the vowels and accents through the proposed system

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.

scholarly journals Recent Progress in the CUHK Dysarthric Speech Recognition System

2021 ◽  
pp. 1-1
Author(s):  
Shansong Liu ◽  
Shoukang Hu ◽  
Xurong Xie ◽  
Mengzhe Geng ◽  
Mingyu Cui ◽  
...  
Keyword(s):  
Speech Recognition ◽  
Recent Progress ◽  
Recognition System ◽  
Speech Recognition System ◽  
Dysarthric Speech
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