Speaker Identification based on Hybrid Feature Extraction Techniques

被引：0
|
作者：
Abualadas, Feras E. [1 ]
Zeki, Akram M. [2 ]
Al-Ani, Muzhir Shaban [3 ]
Messikh, Az-Eddine [2 ]
机构：
[1] Int Islamic Univ Malaysia, Ajloun Natl Univ, Comp Sci, Jerash, Jordan
[2] Int Islamic Univ Malaysia, Kulliyyah Informat & Commun Technol, Kuala Lumpur, Malaysia
[3] Univ Human Dev, Coll Sci & Technol Comp Sci, Sulaymaniyah Krg, Iraq
来源：
INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2019年 / 10卷 / 03期
关键词：
Speaker identification; biometrics; speaker verification; speaker recognition; text-independent; text-dependent;
D O I：
10.14569/IJACSA.2019.0100342
中图分类号：
TP301 [理论、方法];
学科分类号：
081202 ;
摘要：
One of the most exciting areas of signal processing is speech processing; speech contains many features or characteristics that can discriminate the identity of the person. The human voice is considered one of the important biometric characteristics that can be used for person identification. This work is concerned with studying the effect of appropriate extracted features from various levels of discrete wavelet transformation (DWT) and the concatenation of two techniques (discrete wavelet and curvelet transform) and study the effect of reducing the number of features by using principal component analysis (PCA) on speaker identification. Backpropagation (BP) neural network was also introduced as a classifier.
引用
收藏
页码：322 / 327
页数：6
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