@article{(Open Science Index):https://publications.waset.org/pdf/9982,
	  title     = {Efficient DTW-Based Speech Recognition System for Isolated Words of Arabic Language},
	  author    = {Khalid A. Darabkh and  Ala F. Khalifeh and  Baraa A. Bathech and  Saed W. Sabah},
	  country	= {},
	  institution	= {},
	  abstract     = {Despite the fact that Arabic language is currently one
of the most common languages worldwide, there has been only a
little research on Arabic speech recognition relative to other
languages such as English and Japanese. Generally, digital speech
processing and voice recognition algorithms are of special
importance for designing efficient, accurate, as well as fast automatic
speech recognition systems. However, the speech recognition process
carried out in this paper is divided into three stages as follows: firstly,
the signal is preprocessed to reduce noise effects. After that, the
signal is digitized and hearingized. Consequently, the voice activity
regions are segmented using voice activity detection (VAD)
algorithm. Secondly, features are extracted from the speech signal
using Mel-frequency cepstral coefficients (MFCC) algorithm.
Moreover, delta and acceleration (delta-delta) coefficients have been
added for the reason of improving the recognition accuracy. Finally,
each test word-s features are compared to the training database using
dynamic time warping (DTW) algorithm. Utilizing the best set up
made for all affected parameters to the aforementioned techniques,
the proposed system achieved a recognition rate of about 98.5%
which outperformed other HMM and ANN-based approaches
available in the literature.},
	    journal   = {International Journal of Electrical and Computer Engineering},
	  volume    = {7},
	  number    = {5},
	  year      = {2013},
	  pages     = {586 - 593},
	  ee        = {https://publications.waset.org/pdf/9982},
	  url   	= {https://publications.waset.org/vol/77},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 77, 2013},
	}