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Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm

Classification (of information); Data mining; Digital forensics; Forestry; Learning algorithms; Loudspeakers; Motion compensation; Nearest neighbor search; Speech recognition; Trees (mathematics); K-near neighbor; Logistic model tree; Logistics model; Mel frequency cepstral co-efficient; Mel frequen...

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Main Authors: Abdulwahid S., Mahmoud M.A., Abdulwahid N.
Other Authors: 57361650900
Format: Conference Paper
Published: Springer Science and Business Media Deutschland GmbH 2023
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id my.uniten.dspace-26458
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spelling my.uniten.dspace-264582023-05-29T17:10:45Z Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm Abdulwahid S. Mahmoud M.A. Abdulwahid N. 57361650900 55247787300 57361350200 Classification (of information); Data mining; Digital forensics; Forestry; Learning algorithms; Loudspeakers; Motion compensation; Nearest neighbor search; Speech recognition; Trees (mathematics); K-near neighbor; Logistic model tree; Logistics model; Mel frequency cepstral co-efficient; Mel frequency cepstral coefficient; Mel-frequency cepstral coefficients; Mining classification; Model trees; Nearest-neighbour; Speaker identification systems; Authentication For many years, there was an increasing necessity for being capable of the identification of a person based on his/her voice. Judges, law enforcement agencies, detectives, and lawyers, wanted to be able to use the forensic authentication of voice for investigating a suspect or confirming a judgment of guilt or innocence. This study aims to design and build a comprehensive identification of the forensic speakers for the Arabic language. The suggested system has been utilized for the recognition of forensic speaker�s isolated words for purposes of identification. It comprises two stages; the first stage is training the sentence of the forensic speaker in the case where it is not previously processed and stored; the second stage is testing; it is applied in the case where the sentence of the forensic speaker has been previously processed and stored. Every one of the phases involves utilizing audio features (standard division, mean, amplitude, and zero-crossing), pre-processing with the use of the MFCC, Hamming Window, vector quantization, and data mining classification approaches. The proposed system implementation provides removal of the noise in spoken sentences, processing speech sentences prior to the storing, and a correct classification with the use of a number of algorithms of data mining classification such as the Logistic Model Tree (LMT), and K-nearest neighbor (KNN) algorithms. KNN being given the highest accuracy of 91.53% and 94.56% respectively. � 2021, Springer Nature Switzerland AG. Final 2023-05-29T09:10:45Z 2023-05-29T09:10:45Z 2021 Conference Paper 10.1007/978-3-030-90235-3_40 2-s2.0-85120526935 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85120526935&doi=10.1007%2f978-3-030-90235-3_40&partnerID=40&md5=9fe8d0f362f86233e6c844506d8803a6 https://irepository.uniten.edu.my/handle/123456789/26458 13051 LNCS 459 468 Springer Science and Business Media Deutschland GmbH Scopus
institution Universiti Tenaga Nasional
building UNITEN Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Tenaga Nasional
content_source UNITEN Institutional Repository
url_provider http://dspace.uniten.edu.my/
description Classification (of information); Data mining; Digital forensics; Forestry; Learning algorithms; Loudspeakers; Motion compensation; Nearest neighbor search; Speech recognition; Trees (mathematics); K-near neighbor; Logistic model tree; Logistics model; Mel frequency cepstral co-efficient; Mel frequency cepstral coefficient; Mel-frequency cepstral coefficients; Mining classification; Model trees; Nearest-neighbour; Speaker identification systems; Authentication
author2 57361650900
author_facet 57361650900
Abdulwahid S.
Mahmoud M.A.
Abdulwahid N.
format Conference Paper
author Abdulwahid S.
Mahmoud M.A.
Abdulwahid N.
spellingShingle Abdulwahid S.
Mahmoud M.A.
Abdulwahid N.
Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm
author_sort Abdulwahid S.
title Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm
title_short Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm
title_full Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm
title_fullStr Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm
title_full_unstemmed Arabic Speaker Identification System for Forensic Authentication Using K-NN Algorithm
title_sort arabic speaker identification system for forensic authentication using k-nn algorithm
publisher Springer Science and Business Media Deutschland GmbH
publishDate 2023
_version_ 1806425862322520064
score 13.211869

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