@ARTICLE{ iet:/content/journals/10.1049/iet-bmt.2014.0011, author = {Noor Almaadeed}, affiliation = {Department of Computer Engineering, Brunel University, Kingston Lane, Uxbridge, Middlesex UB8 3PH, UK}, affiliation = {Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar}, author = {Amar Aggoun}, affiliation = {Department of Computer Science and Technology, University of Bedfordshire, University Square, Luton, LU1, 3JU, UK}, author = {Abbes Amira}, affiliation = {Department of Computer Science and Engineering, College of Engineering, Qatar University, Doha, Qatar}, affiliation = {Department of Engineering and Computer Science, University of the West of Scotland, Paisley, UK}, keywords = {multimodal neural networks;Gaussian mixture model;discrete wavelet transform;probabilistic neural networks;MFCC;learning module;back-propagation neural network;text-independent multimodal speaker identification system;GRID database corpora;principal component analysis;majority voting scheme;wavelet subband coding;general regressive neural networks;wavelet analysis;biometric authentication systems;radial basis function neural networks;Mel-frequency cepstral coefficients;wavelet packet transform;}, ISSN = {2047-4938}, language = {English}, abstract = {The rapid momentum of the technology progress in the recent years has led to a tremendous rise in the use of biometric authentication systems. The objective of this research is to investigate the problem of identifying a speaker from its voice regardless of the content. In this study, the authors designed and implemented a novel text-independent multimodal speaker identification system based on wavelet analysis and neural networks. Wavelet analysis comprises discrete wavelet transform, wavelet packet transform, wavelet sub-band coding and Mel-frequency cepstral coefficients (MFCCs). The learning module comprises general regressive, probabilistic and radial basis function neural networks, forming decisions through a majority voting scheme. The system was found to be competitive and it improved the identification rate by 15% as compared with the classical MFCC. In addition, it reduced the identification time by 40% as compared with the back-propagation neural network, Gaussian mixture model and principal component analysis. Performance tests conducted using the GRID database corpora have shown that this approach has faster identification time and greater accuracy compared with traditional approaches, and it is applicable to real-time, text-independent speaker identification systems.}, title = {Speaker identification using multimodal neural networks and wavelet analysis}, journal = {IET Biometrics}, issue = {1}, volume = {4}, year = {2015}, month = {March}, pages = {18-28(10)}, publisher ={Institution of Engineering and Technology}, copyright = {This is an open access article published by the IET under the Creative Commons Attribution License <br/>(<a href="http://creativecommons.org/licenses/by/3.0/" target="_blank">http://creativecommons.org/licenses/by/3.0/</a>)}, url = {https://digital-library.theiet.org/;jsessionid=b1afkp85kbppm.x-iet-live-01content/journals/10.1049/iet-bmt.2014.0011} }