Speaker identification based on hybrid feature extraction techniques
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. Th...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English English English |
| Published: |
The Science and Information Organization
2019
|
| Subjects: | |
| Online Access: | http://irep.iium.edu.my/72390/ http://irep.iium.edu.my/72390/ http://irep.iium.edu.my/72390/ http://irep.iium.edu.my/72390/1/72390%20Speaker%20identification%20based%20on%20hybrid%20feature%20extraction%20techniques.pdf http://irep.iium.edu.my/72390/2/72390%20Speaker%20identification%20based%20on%20hybrid%20feature%20extraction%20techniques%20SCOPUS.pdf http://irep.iium.edu.my/72390/13/72390_Speaker%20Identification%20based%20on%20Hybrid%20Feature%20Extraction%20Techniques_wos.pdf |
| Summary: | 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. |
|---|