Main Page
Welcome
About the Center
About The SPC
Mission and Vision
Organizational Structure
Spc Directors
Facts and Figures
Services
ِActivities
Exhibitions
Print Exhibition
Books Exhibition
Visits
Training
Workshops
Courses
Seminars
Conventions
Celebrations
Research
Favorite Links
Contact Us
PhotoAlbum
Contact Us
Researches
عربي
English
About
Admission
Academic
Research and Innovations
University Life
E-Services
Search
Scientific Publishing Center
Document Details
Document Type
:
Article In Journal
Document Title
:
Comparative Study of ANN and HMM to Arabic Digits Recognition Systems
دراسة مقارنه بين أداء الخلايا العصبية و نموذج ماركوف الخفي في أداء التعرف الآلي على الأرقام العربية المنطوقة
Subject
:
Electricical and Computer Engineering
Document Language
:
English
Abstract
:
Arabic language is a Semitic language that has many differences when compared to Latin languages such as English. One of these differences is how to pronounce the ten digits, zero through nine. All Arabic digits are polysyllabic (except digit zero which is a monosyllabic) words and most of them contain Arabic unique phonemes, namely, pharyngeal and emphatic subset. In a previous paper the researcher designed an Artificial Neural Networks (ANN) based Arabic digits recognition system. In this paper we continued the research by designing Hidden Markov Model (HMM) based system that was designed and tested with automatic Arabic digits recognition. The old system was isolated whole word speech recognizer, but the current one was an isolated word phoneme based recognizer. Both systems were implemented both as a multi-speaker (i.e., the same set of speakers were used in both the training and testing phases) mode and speaker-independent (i.e., speakers used for training are different from those used for testing) mode. The main aim of this paper was to compare, analyze, and discuss the outcomes of these two recognition systems. The ANN based recognition system achieved 99.5% correct digit recognition in the case of multi-speaker mode, and 94.5% in the case of speaker-independent mode. On the other hand, the HMM based recognition system achieved 98.1% correct digit recognition in the case of multi-speaker mode, and 94.8% in the case of speaker-independent mode.
ISSN
:
1319-1047
Journal Name
:
Engineering Sciences Journal
Volume
:
19
Issue Number
:
1
Publishing Year
:
1429 AH
2008 AD
Number Of Pages
:
17
Article Type
:
Article
Added Date
:
Sunday, October 11, 2009
Researchers
Researcher Name (Arabic)
Researcher Name (English)
Researcher Type
Dr Grade
Email
يوسف عجمي العتيبي
Yousef Ajami Alotaibi
Researcher
Files
File Name
Type
Description
23033.pdf
pdf
Back To Researches Page