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    Speaker identification using multimodal neural networks and wavelet analysis

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    Authors
    Aggoun, Amar
    Almaadeed, Noor
    Amira, Abbes
    Issue Date
    2015-01-19
    
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    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.
    Citation
    Almaadeed, N., Aggoun, A., and Amira, A. (21015)Speaker identification using multimodal neural networks and wavelet analysis, IET Biometrics, 4 (1), pp. 18-28
    Publisher
    IET
    Journal
    IET Biometrics
    URI
    http://hdl.handle.net/2436/620913
    DOI
    10.1049/iet-bmt.2014.0011
    Additional Links
    http://digital-library.theiet.org/content/journals/10.1049/iet-bmt.2014.0011
    Type
    Journal article
    Language
    en
    Description
    © 2014 The Authors. Published by IET. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1049/iet-bmt.2014.0011
    ISSN
    2047-4938
    2047-4946
    ae974a485f413a2113503eed53cd6c53
    10.1049/iet-bmt.2014.0011
    Scopus Count
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    Faculty of Science and Engineering

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