Access Control Using Specific Code and Biometric Identification
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Date
2025
Journal Title
Journal ISSN
Volume Title
Publisher
University of Msila
Abstract
This study aims to develop a verification and identification system for speakers with
intelligent speaker recognition, by relying on MFCC and PLP algorithms coupled with ML
models like SVM, Random Forest, and Neural Networks. The system was then tested on a
database of 24 speakers, where SVM, followed by Neural Networks and Random Forests,
showed best results with PLP features, while Gradient Boosting showed poor results. The study
recommends increasing the database, implementing data augmentation techniques, testing the
models in real-life scenarios, and combining voice with other biometrics for enhanced security.
Description
Keywords
Mel Frequency Cepstral Coefficients, Perceptual Linear Prediction. Support Vector Machine. MFCC. PLP. SVM.