Arabic speaker identification system using multi features
Joint Authors
Ali, Akbas Izz al-Din
Muhammad, Rawiyah A.
Hassan, Nida Falih
Source
Engineering and Technology Journal
Issue
Vol. 38, Issue 5A (31 May. 2020), pp.769-778, 10 p.
Publisher
Publication Date
2020-05-31
Country of Publication
Iraq
No. of Pages
10
Main Subjects
Information Technology and Computer Science
Topics
Abstract EN
The performance regarding the Speaker Identification Systems (SIS) has enhanced because of the current developments in speech processing methods, however, an improvement is still required with regard to text-independent speaker identification in the Arabic language.
In spite of tremendous progress in applied technology for SIS, it is limited to English and some other languages.
This paper aims to design an efficient SIS (text-independent) for the Arabic language.
The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase.
The second phase is testing, which searches the identification of the speaker.
In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency.
Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process.
The best classification technique is a K-nearest neighbors, where it gives higher precision of tremendous progress in applied technology for SIS, it is limited to English and some other languages.
This paper aims to design an efficient SIS (text-independent) for the Arabic language.
The proposed system uses speech signal features for speaker identification purposes, and it includes two phases: The first phase is training, in this phase a corpus of reference database is built which will serve as a reference for comparing and identifying the speaker for the second phase.
The second phase is testing, which searches the identification of the speaker.
In this system, the features will be extracted according to: Mel Frequency Cepstrum Coefficient (MFCC), mathematical calculations of voice frequency and voice fundamental frequency.
Machine learning classification techniques: K-nearest neighbors, Sequential Minimum Optimization and Logistic Model Tree are used in the classification process.
The best classification technique is a K-nearest neighbors, where it gives higher precision 94.8% .
American Psychological Association (APA)
Muhammad, Rawiyah A.& Hassan, Nida Falih& Ali, Akbas Izz al-Din. 2020. Arabic speaker identification system using multi features. Engineering and Technology Journal،Vol. 38, no. 5A, pp.769-778.
https://search.emarefa.net/detail/BIM-1236567
Modern Language Association (MLA)
Muhammad, Rawiyah A.…[et al.]. Arabic speaker identification system using multi features. Engineering and Technology Journal Vol. 38, no. 5A (2020), pp.769-778.
https://search.emarefa.net/detail/BIM-1236567
American Medical Association (AMA)
Muhammad, Rawiyah A.& Hassan, Nida Falih& Ali, Akbas Izz al-Din. Arabic speaker identification system using multi features. Engineering and Technology Journal. 2020. Vol. 38, no. 5A, pp.769-778.
https://search.emarefa.net/detail/BIM-1236567
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references : p. 777-778
Record ID
BIM-1236567