Classification of Emphatic Consonants and Their Counterparts in Modern Standard Arabic Using Neural Networks

被引：0

作者：

Seddiq, Yasser M. ^{[1
,2
]}

Alotaibi, Yousef A. ^{[1
]}

Selouani, Sid-Ahmed ^{[3
]}

机构：

[1] King Saud Univ, Coll Comp & Informat Sci, Riyadh, Saudi Arabia

[2] KACST, Natl Ctr Elect & Photon, Riyadh, Saudi Arabia

[3] Univ Moncton, LARIHS Lab, Moncton, NB E1A 3E9, Canada

来源：

2014 IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY (ISSPIT) | 2014年

关键词：

Speech classification; MSA; consonants; counterparts; artificial neural networks;

D O I：

暂无

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

This paper presents the work of acoustic analysis related to Modern Standard Arabic (MSA). The problem of classifting the consonant counterparts in MSA is tackled here. The study considers four phonemes: /d(zeta), partial derivative(zeta)/ and their non-emphatic counterparts /d, partial derivative/ respectively. An accurate automatic classification for those phonemes is to be achieved. Artificial neural networks (ANNs) are used for that purpose. The multilayer perceptron (MLP) is applied to the features extracted from the speech signals. The speech utterances used in this study are from KAPD database. Classification accuracy of 83.9% was achieved.

引用

页码：73 / 77

页数：5

共 50 条

[41] RECOGNITION OF ARABIC HANDWRITTEN CHARACTERS USING RESIDUAL NEURAL NETWORKS
Al-Taani, Ahmad T.
Ahmad, Sadeem T.
JORDANIAN JOURNAL OF COMPUTERS AND INFORMATION TECHNOLOGY, 2021, 7 (02): : 192 - 205
[42] Automatic processing of handwritten Arabic forms using neural networks
Asiri, A
Khorsheed, MS
ENFORMATIKA, VOL 7: IEC 2005 PROCEEDINGS, 2005, : 313 - 317
[43] An approach to offline Arabic Character recognition using neural networks
Nawaz, SN
Sarfraz, M
Zidouri, A
Al-Khatib, WG
ICECS 2003: PROCEEDINGS OF THE 2003 10TH IEEE INTERNATIONAL CONFERENCE ON ELECTRONICS, CIRCUITS AND SYSTEMS, VOLS 1-3, 2003, : 1328 - 1331
[44] EFFICIENT ARABIC EMOTION RECOGNITION USING DEEP NEURAL NETWORKS
Hifny, Yasser
Ali, Ahmed
2019 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP), 2019, : 6710 - 6714
[45] An approach to offline Arabic character recognition using neural networks
1600, Emirates Telecommunications Corporation (ETISALAT); Etisalat College of Engineering (ECE); IEEE Circuits and Systems Society (CAS); Institute of Electrical and Electronics Engineers (IEEE); University of Sharjah (UOS) (Institute of Electrical and Electronics Engineers Inc., United States):
[46] Automatic diacritization of Arabic text using recurrent neural networks
Abandah, Gheith A.
Graves, Alex
Al-Shagoor, Balkees
Arabiyat, Alaa
Jamour, Fuad
Al-Taee, Majid
INTERNATIONAL JOURNAL ON DOCUMENT ANALYSIS AND RECOGNITION, 2015, 18 (02) : 183 - 197
[47] Spoken arabic digits recognizer using recurrent neural networks
Alotaibi, YA
PROCEEDINGS OF THE FOURTH IEEE INTERNATIONAL SYMPOSIUM ON SIGNAL PROCESSING AND INFORMATION TECHNOLOGY, 2004, : 195 - 199
[48] Arabic Text Classification Using Convolutional Neural Network and Genetic Algorithms
Alsaleh, Deem
Larabi-Marie-Sainte, Souad
IEEE ACCESS, 2021, 9 (09): : 91670 - 91685
[49] THE UNPREDICTABILITY OF STANDARD BACK-PROPAGATION NEURAL NETWORKS IN CLASSIFICATION APPLICATIONS
WANG, SH
MANAGEMENT SCIENCE, 1995, 41 (03) : 555 - 559
[50] Neurologist Standard Classification of Facial Nerve Paralysis with Deep Neural Networks
Song, Anping
Wu, Zuoyu
Ding, Xuehai
Hu, Qian
Di, Xinyi
FUTURE INTERNET, 2018, 10 (11):

← 1 2 3 4 5 →