Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus
This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences record...
Saved in:
Main Authors: | , , , , |
---|---|
Format: | Article |
Language: | English English |
Published: |
Zarqa Private University, Jordan
2012
|
Subjects: | |
Online Access: | http://irep.iium.edu.my/6980/1/Arabic_Speaker-Independent_Continuous__Arab_Journal.pdf http://irep.iium.edu.my/6980/4/Arabic_Speaker-Independent_Continuous_.pdf http://irep.iium.edu.my/6980/ http://www.iajit.org/ |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
id |
my.iium.irep.6980 |
---|---|
record_format |
dspace |
spelling |
my.iium.irep.69802020-10-23T01:05:51Z http://irep.iium.edu.my/6980/ Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran T Technology (General) T10.5 Communication of technical information This paper describes and proposes an efficient and effective framework for the design and development of a speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world. The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models (HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of 11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of 89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively. Zarqa Private University, Jordan 2012-01 Article PeerReviewed application/pdf en http://irep.iium.edu.my/6980/1/Arabic_Speaker-Independent_Continuous__Arab_Journal.pdf application/pdf en http://irep.iium.edu.my/6980/4/Arabic_Speaker-Independent_Continuous_.pdf Abushariah, Mohammad Abd-Alrahman Mahmoud and -, Raja Ainon and Zainuddin, Roziati and Elshafei, Moustafa and Khalifa, Othman Omran (2012) Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus. International Arab Journal of Information Technology (IAJIT), 9 (1). pp. 84-93. ISSN 1683-3198 http://www.iajit.org/ |
institution |
Universiti Islam Antarabangsa Malaysia |
building |
IIUM Library |
collection |
Institutional Repository |
continent |
Asia |
country |
Malaysia |
content_provider |
International Islamic University Malaysia |
content_source |
IIUM Repository (IREP) |
url_provider |
http://irep.iium.edu.my/ |
language |
English English |
topic |
T Technology (General) T10.5 Communication of technical information |
spellingShingle |
T Technology (General) T10.5 Communication of technical information Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran Arabic Speaker-Independent Continuous Automatic Speech Recognition Based on a Phonetically Rich and Balanced Speech Corpus |
description |
This paper describes and proposes an efficient and effective framework for the design and development of a
speaker-independent continuous automatic Arabic speech recognition system based on a phonetically rich and balanced
speech corpus. The speech corpus contains a total of 415 sentences recorded by 40 (20 male and 20 female) Arabic native
speakers from 11 different Arab countries representing the three major regions (Levant, Gulf, and Africa) in the Arab world.
The proposed Arabic speech recognition system is based on the Carnegie Mellon University (CMU) Sphinx tools, and the
Cambridge HTK tools were also used at some testing stages. The speech engine uses 3-emitting state Hidden Markov Models
(HMM) for tri-phone based acoustic models. Based on experimental analysis of about 7 hours of training speech data, the
acoustic model is best using continuous observation’s probability model of 16 Gaussian mixture distributions and the state
distributions were tied to 500 senones. The language model contains both bi-grams and tri-grams. For similar speakers but
different sentences, the system obtained a word recognition accuracy of 92.67% and 93.88% and a Word Error Rate (WER) of
11.27% and 10.07% with and without diacritical marks respectively. For different speakers with similar sentences, the system
obtained a word recognition accuracy of 95.92% and 96.29% and a WER of 5.78% and 5.45% with and without diacritical
marks respectively. Whereas different speakers and different sentences, the system obtained a word recognition accuracy of
89.08% and 90.23% and a WER of 15.59% and 14.44% with and without diacritical marks respectively. |
format |
Article |
author |
Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran |
author_facet |
Abushariah, Mohammad Abd-Alrahman Mahmoud -, Raja Ainon Zainuddin, Roziati Elshafei, Moustafa Khalifa, Othman Omran |
author_sort |
Abushariah, Mohammad Abd-Alrahman Mahmoud |
title |
Arabic Speaker-Independent Continuous
Automatic Speech Recognition Based on a
Phonetically Rich and Balanced Speech Corpus |
title_short |
Arabic Speaker-Independent Continuous
Automatic Speech Recognition Based on a
Phonetically Rich and Balanced Speech Corpus |
title_full |
Arabic Speaker-Independent Continuous
Automatic Speech Recognition Based on a
Phonetically Rich and Balanced Speech Corpus |
title_fullStr |
Arabic Speaker-Independent Continuous
Automatic Speech Recognition Based on a
Phonetically Rich and Balanced Speech Corpus |
title_full_unstemmed |
Arabic Speaker-Independent Continuous
Automatic Speech Recognition Based on a
Phonetically Rich and Balanced Speech Corpus |
title_sort |
arabic speaker-independent continuous
automatic speech recognition based on a
phonetically rich and balanced speech corpus |
publisher |
Zarqa Private University, Jordan |
publishDate |
2012 |
url |
http://irep.iium.edu.my/6980/1/Arabic_Speaker-Independent_Continuous__Arab_Journal.pdf http://irep.iium.edu.my/6980/4/Arabic_Speaker-Independent_Continuous_.pdf http://irep.iium.edu.my/6980/ http://www.iajit.org/ |
_version_ |
1681489268497711104 |
score |
13.18916 |