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Speaker identification using multimodal neural networks and wavelet analysis

  • Noor Almaadeed
    ,
  • Amar Aggoun
    ,
  • Abbes Amira
  • Qatar University
    ,
  • Brunel University London
    ,
  • University of the West of Scotland
Research Output: Contribution to journal Article Peer-review

Open access

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.

Publication Information

Output type

Research Output: Contribution to journal Article Peer-review

Original language

English

Pages from-to (Number of pages)

Pages 18-28

Journal (Volume, Issue Number)

IET Biometrics (Volume 4, Issue 1)

Publication milestones

  • Published - 19/03/2015

Publication status

Published - 19/03/2015

ISSN

2047-4938

External Publication IDs

  • handle.net: 10547/624283
  • Scopus: 84924955654

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