Arabic Speaker Identification System Using Multi Features
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 i...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Unviversity of Technology- Iraq
2020-05-01
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| Series: | Engineering and Technology Journal |
| Subjects: | |
| Online Access: | https://etj.uotechnology.edu.iq/article_168960_f2bbf55e8d5b3108f67d84bcbc6e2854.pdf |
| _version_ | 1797336423954644992 |
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| author | Rawia Mohammed Nidaa Hassan Akbas Ali |
| author_facet | Rawia Mohammed Nidaa Hassan Akbas Ali |
| author_sort | Rawia Mohammed |
| collection | DOAJ |
| description | 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 94.8%. |
| first_indexed | 2024-03-08T08:53:09Z |
| format | Article |
| id | doaj.art-3313fb35ba89439aa80617acf0c0f1eb |
| institution | Directory Open Access Journal |
| issn | 1681-6900 2412-0758 |
| language | English |
| last_indexed | 2024-03-08T08:53:09Z |
| publishDate | 2020-05-01 |
| publisher | Unviversity of Technology- Iraq |
| record_format | Article |
| series | Engineering and Technology Journal |
| spelling | doaj.art-3313fb35ba89439aa80617acf0c0f1eb2024-02-01T07:32:52ZengUnviversity of Technology- IraqEngineering and Technology Journal1681-69002412-07582020-05-01385A76977810.30684/etj.v38i5A.408168960Arabic Speaker Identification System Using Multi FeaturesRawia Mohammed0Nidaa Hassan1Akbas Ali2Department of Computer Science, University of Technology, Baghdad, Iraq.Department of Computer Science, University of Technology, Baghdad, Iraq.Department of Computer Science, University of Technology, Baghdad, Iraq.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 94.8%.https://etj.uotechnology.edu.iq/article_168960_f2bbf55e8d5b3108f67d84bcbc6e2854.pdfmfccspeaker identification systemspeech signalk-nearest neighbors and sequential minimum optimization |
| spellingShingle | Rawia Mohammed Nidaa Hassan Akbas Ali Arabic Speaker Identification System Using Multi Features Engineering and Technology Journal mfcc speaker identification system speech signal k-nearest neighbors and sequential minimum optimization |
| title | Arabic Speaker Identification System Using Multi Features |
| title_full | Arabic Speaker Identification System Using Multi Features |
| title_fullStr | Arabic Speaker Identification System Using Multi Features |
| title_full_unstemmed | Arabic Speaker Identification System Using Multi Features |
| title_short | Arabic Speaker Identification System Using Multi Features |
| title_sort | arabic speaker identification system using multi features |
| topic | mfcc speaker identification system speech signal k-nearest neighbors and sequential minimum optimization |
| url | https://etj.uotechnology.edu.iq/article_168960_f2bbf55e8d5b3108f67d84bcbc6e2854.pdf |
| work_keys_str_mv | AT rawiamohammed arabicspeakeridentificationsystemusingmultifeatures AT nidaahassan arabicspeakeridentificationsystemusingmultifeatures AT akbasali arabicspeakeridentificationsystemusingmultifeatures |