CFP last date
20 January 2025
Call for Paper
February Edition
IJCA solicits high quality original research papers for the upcoming February edition of the journal. The last date of research paper submission is 20 January 2025

Submit your paper
Know more
The week's pick
Responsive image
SFLA-Based Line Balancing and Task Optimization in Master-Slave Controlled Hanger Transportation Systems for Garment Production

Duc Hoang Nguyen
Random Articles
Reseach Article

Real-Time Arabic Speech Recognition

by Zaid Y. Mohammed, Abdul Sattar M. Khidhir
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 81 - Number 4
Year of Publication: 2013
Authors: Zaid Y. Mohammed, Abdul Sattar M. Khidhir
10.5120/14003-2048

Zaid Y. Mohammed, Abdul Sattar M. Khidhir . Real-Time Arabic Speech Recognition. International Journal of Computer Applications. 81, 4 ( November 2013), 43-45. DOI=10.5120/14003-2048

@article{ 10.5120/14003-2048,
author = { Zaid Y. Mohammed, Abdul Sattar M. Khidhir },
title = { Real-Time Arabic Speech Recognition },
journal = { International Journal of Computer Applications },
issue_date = { November 2013 },
volume = { 81 },
number = { 4 },
month = { November },
year = { 2013 },
issn = { 0975-8887 },
pages = { 43-45 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume81/number4/14003-2048/ },
doi = { 10.5120/14003-2048 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-06T21:55:13.411399+05:30
%A Zaid Y. Mohammed
%A Abdul Sattar M. Khidhir
%T Real-Time Arabic Speech Recognition
%J International Journal of Computer Applications
%@ 0975-8887
%V 81
%N 4
%P 43-45
%D 2013
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Speech recognition system needs to perform a high complex calculation and short time to complete it. This is a big challenge for the real-time systems. However, using a simple and fast algorithm may do this task for the slow systems. Thus, the main objective of this paper is to design and implement a Real-Time Arabic Speech Recognition system using MATLAB environment. It is capable of accurately identifying some letters while remaining simple and fast. It uses the Mel-Frequency Cepstral Coefficients (MFCCs) as a feature extraction and Euclidean distance to compare the test sound and the database. A recognition rate of 89. 6% has been reached.

References
  1. S. J. Melnikoff, S. F. Quigley and M. J. Russell, Implementing a simple continuous speech recognition system on an FPGA, Proc. of the 10th Annual IEEE Symposium on Field-Programmable Custom Computing Machines, pp. 275-276, 2002.
  2. J. R. Deller, J. H. L. Hansen and J. G. Proakis, "Discrete-Time Processing of Speech Signals", IEEE Press, 2000, 56-63 380-385
  3. S. Sakti, K. Markov, S. Nakamura, W. Minker, "Incorporating Knowledge Sources into Statistical Speech Recognition", Springer, 2009, page 39-40.
  4. Lawrence rabinar,biing-hwangjuang, "fundamental of speech recognition ",prentice hall,1993.
  5. L. Deng and D. O'Shaughnessy, Speech Processing A Dynamic and Optimization-Oriented Approach, Marcel Dekker, New York, 2003.
  6. W. C. Chu, Speech Coding Algorithms, John Wiley and Sons, Wiley-IEEE, 2003.
  7. L. Muda, M. Begam, I. Elamvazuthi, " Voice Recognition Algorithms using MelFrequency Cepstral Coefficient (MFCC) andDynamic Time Warping (DTW) Techniques", Journal of Computing, Volume 2, Issue 3, March 2010, ISSN 2151-9617
  8. X. Huang, A. Acero and H. Wuenon, Spoken Language Processing: A Guide to Theory, Algorithm and System Development, Pearson, 2005.
  9. R. Vergin, "An algorithm for robust signal modeling in speech recognition," Proceedings of the 1998 IEEE International Conference on Acoustics, Speech, and Signal Processing (ICASSP '98), Vol. 2, pp. 969-972, May, 1998.
  10. B. P. Lathi, "Modern Digital and Analog Communication Systems", California state universtiy,1998
  11. Wei Han, Cheong-Fat Chan, Chiu-Sing Choy and Kong-Pang Pun "An efficient MFCC extraction method in speech recognition" Department of Electronic Engineering" The Chinese University of Hong Kong, Hong, IEEE – ISCAS, 2006.
  12. Steven B. Davis, and Paul Mermelstein, Comparison of parametricrepresentations for monosyllabic word recognition in continuously spoken sentences. IEEE Trans. ASSP, 1980.
  13. R. S. Kurcan, "Isolated Word Recognition From in-ear Microphone Data Using Hidden Markov Models (HMM)",M. Sc. Thisis,2006,178 pp.
  14. Stephen E. Levinson, "Mathematical Models for Speech Technology", John Wiley&Sons,ltd, University of Illinois at Urbana-Champaign, USA,2005.
Index Terms

Computer Science
Information Sciences

Keywords

Feature extraction Mel-Frequency Cepstral Coefficients (MFCCs) Feature match.

Powered by PhDFocusTM