Möchten Sie über diese Zeitschrift informiert bleiben? Klicken Sie bitte auf die Buttons, um unsere Alerts zu abonnieren.
Möchten Sie über diese Zeitschrift informiert bleiben? Klicken Sie bitte auf die Buttons, um unsere Alerts zu abonnieren.
The Problems, Issues and Future Challenges of Automatic Speech Recognition for Quranic verse Recitation: A Review
in Al-Bayan: Journal of Qur'an and Hadith StudiesSearch for other papers by Noor Jamaliah Ibrahim in
Current site
Google Scholar
Search for other papers by Mohd Yamani Idna Idris in
Current site
Google Scholar
Search for other papers by M.Y. Zulkifli Mohd Yusoff in
Current site
Google Scholar
Search for other papers by Asma Anuar in
Current site
Google Scholar
Research into Automatic Speech Recognition (asr) software for the Arabic language has gained significance in recent years. Since the Qurʾan is in Arabic, the rendering of it into Arabic asr may appear difficult and quite challenging. Furthermore, the way in which any Qurʾanic verse is recited can differ from one person to another, as can even the same āya (verse), as this is totally dependent on the reciters’ level of understanding of tajwīd (pronunciation rules while reading the recitation of the Qurʾan) while delivering the āya. In this paper, we provide a comprehensive review of the challenges for developing Qurʾanic verse recitation recognition software, focused on the tajwīd rules for checking features and language. Other related issues that fall under Qurʾanic linguistic aspects and properties, including recitation errors (common/possible mistakes made while reading) of passages, are also discussed in this paper. Further areas of potential expansion, new ideas, and new areas of research for supporting Qurʾanic learning for the Muslim community are also explored and identified. Thus, this paper will allow the field to be expanded and developed, all of which focusses on improving Qurʾanic learning process through a talaqqī and mushāfaha method of Qurʾan recitation.
Kauf
Sofortzugang erwerben (PDF-Download und unbegrenzter Online-Zugang):
Institutszugang
Melden Sie sich mit Open Athens, Shibboleth oder Ihren institutionellen Anmeldedaten an.
Persönliche Anmeldung
Melden Sie sich mit Ihrem brill.com-Konto an
-
1
Eldeen, S., & Morsi, H. (2005). Computer Aided Pronunciation Learning System using Statistical based Automatic System using Statistical based Automatic Speech Recognition Techniques. (PhD), Cairo University.
-
5
N.J. Ibrahim, Z.M. Yusoff, and Z. Razak, “Improve Design for Automated Tajweed Checking Rules Engine of Quranic Verse Recitation: A Review,” QURANICA, International Journal of Quranic Research, 1(1), (2011): 39-50.
-
11
K. Kirchhoff et al., “Morphology-Based Language Modeling for Conversational Arabic Speech Recognition,” Computer Speech and Language, 20(4), (2006): 589-608.
-
13
Kirchhoff et al., “Morphology-Based Language Modeling for conversational Arabic speech recognition”. Computer Speech and Language, 20 (4), (2006): 589-608; Tomokiyo et al., Arabic in My Hand: Small-Footprint Synthesis of Egyptian Arabic. Paper presented at the EUROSPEECH 2003, Geneva.
-
14
T. Hassan, A.-F. Wassim and M. Bassem, “Analysis and Implementation of an Automated Delimiter of ‘Quranic’ Verses in Audio Files using Speech Recognition Techniques,” in Robust Speech Recognition and Understanding, ed. Michael Grimm and Kristian Kroschel (Vienna, Austria: I-Tech, 2007), 351-362; M. Maamouri, A. Bies and S. Kulick, Diacritization to Arabic Treebank Annotation and Parsing. Paper presented at the Proceedings of the Conference of the Machine Translation sig (2006); J. Park et al., Training and Adapting mlp Features for Arabic Speech Recognition, Paper presented at the ieee International Conference on Acoustics, Speech and Signal Processing, 2009 – icassp 2009; Kirchhoff, Morphology-Based Language Modeling. Kirchhoff et al., “Morphology-Based Language Modeling for conversational Arabic speech recognition”. Computer Speech and Language, 20(4), (2006): 589-608.
-
15
E. Mourtaga, A. Sharieh and M. Abdallah, Speaker Independent Quranic Recognizer based on Maximum Likelihood Linear Regression. Paper presented at the World Academy of Science, Engineering and Technology, 2007.
-
21
M.E. Ahmed, “Towards An Arabic Text-To-Speech System,” The Arabian Journal for Science and Engineering, 16(4B), (1991): 565-583; Selouani and Alotaibi, “Adaptation of Foreign Accented Speakers in native Arabic asr systems”. Applied Computing and Informatics, 9(1), (2011): 1-10; A. Youssef and O. Emam, “An Arabic tts System Based on the ibm Trainable Speech Synthesizer,” in The Automatic Processing of Arabic, jep-taln (April, 2004).
-
22
M.E. Ahmed, “Towards An Arabic Text-To-Speech System,” The Arabian Journal for Science and Engineering, 16(4B), (1991): 565-583.
-
31
Y.A. El-Imam, “Phonetization of Arabic: Rules and Algorithms,” Computer Speech & Language, 18(4), (2004): 339-73.
-
33
R.L. Trask, A Dictionary of Phonetics and Phonology (New York: Routledge, 1996).
-
49
Y.O.M. Elhadj, “E-HALAGAT: An E-Learning System for Teaching the Holy Quran,” Turkish Online Journal of Educational Technology, 9(1), (2010): 54-61.
-
50
A. Muhammad et al., “E-Hafiz: Intelligent System to Help Muslims in Recitation and Memorization of Quran,” Life Science Journal, 9(1), (2012): 534-541.
| Insgesamt | Letzte 365 Tage | In den letzten 30 Tagen | |
|---|---|---|---|
| Aufrufe von Kurzbeschreibungen | 1062 | 236 | 25 |
| Gesamttextansichten | 156 | 2 | 0 |
| PDF-Downloads | 57 | 5 | 0 |
The Problems, Issues and Future Challenges of Automatic Speech Recognition for Quranic verse Recitation: A Review
in Al-Bayan: Journal of Qur'an and Hadith StudiesSearch for other papers by Noor Jamaliah Ibrahim in
Current site
Google Scholar
PubMed
Search for other papers by Mohd Yamani Idna Idris in
Current site
Google Scholar
PubMed
Search for other papers by M.Y. Zulkifli Mohd Yusoff in
Current site
Google Scholar
PubMed
Search for other papers by Asma Anuar in
Current site
Google Scholar
PubMed
Research into Automatic Speech Recognition (asr) software for the Arabic language has gained significance in recent years. Since the Qurʾan is in Arabic, the rendering of it into Arabic asr may appear difficult and quite challenging. Furthermore, the way in which any Qurʾanic verse is recited can differ from one person to another, as can even the same āya (verse), as this is totally dependent on the reciters’ level of understanding of tajwīd (pronunciation rules while reading the recitation of the Qurʾan) while delivering the āya. In this paper, we provide a comprehensive review of the challenges for developing Qurʾanic verse recitation recognition software, focused on the tajwīd rules for checking features and language. Other related issues that fall under Qurʾanic linguistic aspects and properties, including recitation errors (common/possible mistakes made while reading) of passages, are also discussed in this paper. Further areas of potential expansion, new ideas, and new areas of research for supporting Qurʾanic learning for the Muslim community are also explored and identified. Thus, this paper will allow the field to be expanded and developed, all of which focusses on improving Qurʾanic learning process through a talaqqī and mushāfaha method of Qurʾan recitation.
| Insgesamt | Letzte 365 Tage | In den letzten 30 Tagen | |
|---|---|---|---|
| Aufrufe von Kurzbeschreibungen | 1062 | 236 | 25 |
| Gesamttextansichten | 156 | 2 | 0 |
| PDF-Downloads | 57 | 5 | 0 |
