Speaker identification using multimodal neural networks and wavelet analysis
Affiliation
Brunel UniversityQatar University
University of Bedfordshire
University of the West of Scotland
Issue Date
2015-03-19
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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, Amira A (2015) 'Speaker identification using multimodal neural networks and wavelet analysis', IET Biometrics, 4 (1), pp.18-28.Journal
IET BiometricsAdditional Links
https://ieeexplore.ieee.org/document/7062142Type
ArticleLanguage
enISSN
2047-4938ae974a485f413a2113503eed53cd6c53
10.1049/iet-bmt.2014.0011
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