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

scholarly journals Speech Recognition of Isolated Words using a New Speech Database in Sylheti

2019 ◽  
Vol 8 (3) ◽  
pp. 6259-6268
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Speech Recognition ◽  
Recognition System ◽  
Speech Recognition System ◽  
Natural Languages ◽  
Speech Database ◽  
Recognition Systems ◽  
Neural Network Classifiers ◽  
Engineering Psychology

With the advancements in the field of artificial intelligence, speech recognition based applications are becoming more and more popular in the recent years. Researchers working in many areas including linguistics, engineering, psychology, etc. have been trying to address various aspects relating to speech recognition in different natural languages around the globe. Although many interactive speech applications in "well-resourced" major languages are being developed, uses of these applications are still limited due to language barrier. Hence, researchers have also been concentrating to design speech recognition system in various under-resourced languages. Sylheti is one of such under-resourced languages primarily spoken in the Sylhet division of Bangladesh and also spoken in the southern part of Assam, India. This paper has two contributions: i) it presents a new speech database of isolated words for the Sylheti language, and ii) it presents speech recognition systems for the Sylheti language to recognize isolated Sylheti words by applying two variants of neural network classifiers. The performances of these recognition systems are evaluated with the proposed database and the observations are presented.

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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