Quran Companion – A helping tool for Huffaz

Muhammad Rafi

Abstract


Abstract— This paper presents work on a novel and innovative mobile application for Huffaz who find it difficult to maintain their Hifz in professional life. It is because they require a partner (Hafiz) who can listen to their recitation of Quran and correct it (if required). An application that listens Quran recitation and indicate mistakes (if any) with a feedback mechanism for their correction can solve the problem. A variety of methods related to Speech Recognition and Supervised Learning have been explored to develop such an invention. The way is to compare the input (speech) with standard (speech or text), outputting the status. However, the accuracy level obtained this way is not very reliable. Even so, it is inferable that this extraordinary application is a major step towards effective human-computer interaction. It is important to note that this experimentation is not concerned with teaching people how to recite Quran but with helping people (who already know how to recite Quran) to memorize and revise the Quran in a better way.


References


Viswanath Subramanian Ramesh*, P. Saranya and Ruchira Kapoor “Speech Recognition based Adaptive Examination Application for Visually Impaired Students.” Indian Journal of Science and Technology, Vol 9(37), DOI: 10.17485/ijst/2016/v9i37/102102, October 2016 [2] Haridas, Arul Valiyavalappil, Ramalatha Marimuthu, and Vaazi Gangadharan Sivakumar. “A critical review and analysis on techniques of speech recognition: The road ahead.” International Journal of Knowledgebased and Intelligent Engineering Systems 22.1 (2018): 39-57 [3] Yousfi, Bilal, and Akram M. Zeki. “Holy Qur’an speech recognition system distinguishing the type of recitation.” 2016 7th International Conference on Computer Science and Information Technology (CSIT). IEEE, 2016 [4] Mohamed, H. R. “Arabic Speech Recognition. [5] Cunningham, Pádraig, Matthieu Cord, and Sarah Jane Delany. “Supervised learning.” Machine learning techniques for multimedia. Springer, Berlin, Heidelberg, 2008. 21-49 [6] Hussain, Amir, and Erik Cambria. “Semi-supervised learning for big social data analysis.” Neurocomputing 275 (2018): 1662-1673 [7] Kansiz, A. Oguz, M. Amac Guvensan, and H. Irem Turkmen. “Selection of time-domain features for fall detection based on supervised learning.” Proceedings of the World Congress on Engineering and Computer Science, San Francisco, CA, USA. Vol. 2325. 2013. [8] Khan, Muhammad Khurram, and Yasser M. Alginahi. “The holy Quran digitization: Challenges and concerns.” Life Science Journal 10.2 (2013): 156-164. [9] Talib, Shuhaili, et al. “Mobile Quran app security vulnerabilities.” Proc. 5th Int. Conf. Comput. Informatics, ICOCI. No. 198. 2015 [10] Mukherjee, Shubhankar, Jyoti Prakash, and Deepak Kumar. “Android application development & its security.” Int. J. Comput. Sci. Mobile Comput. 4.3 (2015): 714-719 [11] Zahari, Nuril Ham Al Hafizah Binti, Sharifah Norshah Bani Binti Syed Bidin, and Syadiah Nor Binti Wan Syamsuddin. “Development of Al-Quran Android Application from Year 2013 To 2016: The Highlight.” International Journal of Academic Research in Business and Social Sciences 7.6 (2017): 183-195.


Refbacks

  • There are currently no refbacks.
















 


IBT- Journal of Business Studies by ILMA University
is licensed under a Creative Commons Attribution 4.0 International License.
Based on a work at http://jms.ilmauniversity.edu.pk/index.php/jbs/index.
Permissions beyond the scope of this license may be available at http://ibtjbs.ilmauniversity.edu.pk/