GA-based feature subset selection: Application to Arabic speaker recognition system

Harrag, Abdelghani; Saigaa, Djamel; Boukharouba, K.; Drif, M.; Bouchelaghem, A.

doi:10.1109/his.2011.6122136

Cited by 5 publications

(2 citation statements)

References 9 publications

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“…At the feature generation stage, a maximally large set of distinctive features was extracted, which may be used for the purposes of the ASR System. According to global research, the use of a maximally large set of features does not always ensure that the best results are obtained [ 26 , 27 , 28 ]. Feature selection often enables the obtaining of greater or the same accuracy of classification for a reduced features’ vector, which in turn translates into a significant shortening of the calculation time and a simplifying of the classifier itself.…”

Section: Methodsmentioning

confidence: 99%

Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features

Kamiński

Dobrowolski

2022

Sensors

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This article presents the Automatic Speaker Recognition System (ASR System), which successfully resolves problems such as identification within an open set of speakers and the verification of speakers in difficult recording conditions similar to telephone transmission conditions. The article provides complete information on the architecture of the various internal processing modules of the ASR System. The speaker recognition system proposed in the article, has been compared very closely to other competing systems, achieving improved speaker identification and verification results, on known certified voice dataset. The ASR System owes this to the dual use of genetic algorithms both in the feature selection process and in the optimization of the system’s internal parameters. This was also influenced by the proprietary feature generation and corresponding classification process using Gaussian mixture models. This allowed the development of a system that makes an important contribution to the current state of the art in speaker recognition systems for telephone transmission applications with known speech coding standards.

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Section: Methodsmentioning

confidence: 99%

Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features

Kamiński

Dobrowolski

2022

Sensors

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“…Worldwide research shows that using the maximum set of features does not always produce the best results [33][34][35][36]. Feature selection often offers the possibility of obtaining higher or the same classification accuracy for a reduced feature vector, which in turn translates into reduced computation time.…”

Section: Selection Of Distinctive Featuresmentioning

confidence: 99%

Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication

Kamiński,

Dobrowolski,

Piotrowski

et al. 2023

Electronics

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This paper presents a voice biometrics system implemented in a web application as part of a two-factor authentication (2FA) user login. The web-based application, via a client interface, runs registration, preprocessing, feature extraction and normalization, classification, and speaker verification procedures based on a modified Gaussian mixture model (GMM) algorithm adapted to the application requirements. The article describes in detail the internal modules of this ASR (Automatic Speaker Recognition) system. A comparison of the performance of competing ASR systems using the commercial NIST 2002 SRE voice dataset tested under the same conditions is also presented. In addition, it presents the results of the influence of the application of cepstral mean and variance normalization over a sliding window (WCMVN) and its relevance, especially for voice recordings recorded in varying acoustic tracks. The article also presents the results of the selection of a reference model representing an alternative hypothesis in the decision-making system, which significantly translates into an increase in the effectiveness of speaker verification. The final experiment presented is a test of the performance achieved in a varying acoustic environment during remote voice login to a web portal by the test group, as well as a final adjustment of the decision-making threshold.

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Nature-inspired feature subset selection application to arabic speaker recognition system

Harrag

2014

Int J Speech Technol

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GA-based feature subset selection: Application to Arabic speaker recognition system

Cited by 5 publications

References 9 publications

Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features

Automatic Speaker Recognition System Based on Gaussian Mixture Models, Cepstral Analysis, and Genetic Selection of Distinctive Features

Enhancing Web Application Security: Advanced Biometric Voice Verification for Two-Factor Authentication

Nature-inspired feature subset selection application to arabic speaker recognition system

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