Main Article Content

Abstract

Indonesia is a country with the largest number of Muslims in the world, who read verses of the Qur'an often heard in various public places such as mosques, prayer rooms, and at various activities. Utilization of Automatic Speech Recognition (ASR) as word recognition which aims to find out the verses of the Qur'an that are read to increase knowledge about the verses and other supporting information as a means of preaching in conveying knowledge about the verses of Al-Qur'an. Al-Qur'an. Automatic Speech Recognitions (ASR) is designed using the Python programming language and uses the Django framework to display information about the verses that are read in the form of a web-based display. This research aims to create a technique and system for entering voice commands into machines, so that machines can understand what humans say and obey what they are told. This application converts data into text data using a voice recognition system automatically with a digital audio pattern of spoken words from a speech pattern computer model to produce the final output in the form of text that is stored in the database.

Keywords

Automati Speech Recognition, ASR, Al-Qur’an, Python, Speech Recognition, Django Automation Speech Recognition, ASR, Al-Qur'an, Python, Speech Recognition, Django

Article Details

Author Biographies

Salamun Salamun, Universitas Abdurrab

Teknik Informatika

sukri sukri, Universitas Abdurrab

Teknik Informatika

Khairul Amin, Universitas Abdurrab

Teknik Informatika

Luluk Elvitaria, Universitas Abdurrab

Teknik Informatika

Liza Trisnawati, Universitas Abdurrab

Teknik Informatika
How to Cite
Salamun, S., sukri, sukri, Amin, K., Elvitaria, L., & Trisnawati, L. (2022). Artificial Intelligence Automatic Speech Recognition (ASR) untuk pencarian potongan ayat Al-Qu’ran. Jurnal Komputer Terapan , 8(1), 36–45. https://doi.org/10.35143/jkt.v8i1.5299

References

  1. N. Anggraini, A. Kurniawan, L. K. Wardhani, and N. Hakiem, “Speech recognition application for the speech impaired using the android-based google cloud speech API,” Telkomnika (Telecommunication Comput. Electron. Control., vol. 16, no. 6, pp. 2733–2739, 2018, doi: 10.12928/TELKOMNIKA.v16i6.9638.
  2. F. Gozali and R. S. Suharto, “Pemanfaatan Fitur Google Voice Recognition Pada Smartphone Untuk Pengendalian Peralatan Rumah Tangga,” JETri J. Ilm. Tek. Elektro, vol. 16, no. 2, p. 165, 2019, doi: 10.25105/jetri.v16i2.3620.
  3. P. W. N. Banamtuan, H. Djahi, and A. A. Maggang, “Pemanfaatan Speech Recognition Pad Smartphone Android Sebagai Sistem Pengontrolan Pintu Berbasis Mikrokontroller,” J. Media Elektro, vol. 8, no. 1, pp. 72–78, 2019, doi: 10.35508/jme.v8i1.1421.
  4. Althaf Husein, “Al-Qur’an Di Era Gadget: Studi Deskriptif Aplikasi Qur’an Kemenag,” J. Online Stud. Al-Qur an, vol. 16, no. 1, pp. 55–68, 2020, doi: 10.21009/jsq.016.1.04.
  5. V. Savchenko, “Minimum of Information Divergence Criterion for Signals with Tuning to Speaker Voice in Automatic Speech Recognition,” Radioelectron. Commun. Syst., vol. 63, pp. 42–54, Jan. 2020, doi: 10.3103/S0735272720010045.
  6. T. Zoughi, M. Homayoonpoor, and M. Deypir, “Adaptive Windows Multiple Deep Residual networks for Speech Recognition,” Expert Syst. Appl., vol. 139, p. 112840, Jul. 2019, doi: 10.1016/j.eswa.2019.112840.
  7. A. Joshuva, S. Priyadharsini, S. Aravinth, P. Jayaraman, K. Balachandar, and D. Meganathan, “A review on recent trends and development in speech recognition system,” J. Adv. Res. Dyn. Control Syst., vol. 12, no. 1 Special Issue, pp. 521–528, 2020, doi: 10.5373/JARDCS/V12SP1/20201099.
  8. W. Brundage, “Automatic Speech Recognition and Its Application.,” Int. Res. J. Eng. Technol., vol. 3, no. 5, pp. 247–252, 2016.
  9. Y. R. Dewi, F. Satria, M. Rahayu, V. R. V, and A. Uno, “Implementasi Voice Recognition pada Sistem Pengawasan Anak-Anak Dalam Berkata Kasar Melalui Smartphone Dengan Koneksi WiFi,” Ind. Res. Work. Natl. Semin., vol. 11, no. 1, pp. 20–25, 2020, doi: 10.35313/irwns.v11i1.1962.