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The amount of digital data is increasing every day. At every step of our daily lives, we deal with technologies in which our data are stored (e.g., mobile phones and laptops), and this is one of the main reasons for the design of various types of encryption and user identity verification algorithms. These algorithms are meant not only to fulfill the desire of protecting data but also to address the possibility of granting access of specific digital data to selected individuals. This process brings with it the problem of identity verification. This paper discusses the problem of voice verification and presents a voice verification method based on artificial intelligence methods. Numerous tests are performed herein to demonstrate the effectiveness of the presented solution. The research results are shown and discussed in terms of the advantages and disadvantages of the solution.


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Voice Recognition by Neuro-Heuristic Method

Show Author's information Dawid Połap( )Marcin Woźniak
Institute of Mathematics, Silesian University of Technology, Kaszubska 23, Poland.

Abstract

The amount of digital data is increasing every day. At every step of our daily lives, we deal with technologies in which our data are stored (e.g., mobile phones and laptops), and this is one of the main reasons for the design of various types of encryption and user identity verification algorithms. These algorithms are meant not only to fulfill the desire of protecting data but also to address the possibility of granting access of specific digital data to selected individuals. This process brings with it the problem of identity verification. This paper discusses the problem of voice verification and presents a voice verification method based on artificial intelligence methods. Numerous tests are performed herein to demonstrate the effectiveness of the presented solution. The research results are shown and discussed in terms of the advantages and disadvantages of the solution.

Keywords:

neural network, heuristic algorithms, Discrete Fourier Transform (DFT), image processing
Received: 30 March 2017 Revised: 21 June 2017 Accepted: 29 June 2017 Published: 08 November 2018 Issue date: February 2019
References(20)
[1]
Lu S., Mu T., and Zhang S., A survey on multiview video synthesis and editing, Tsinghua Science and Technology, vol. 21, no. 6, pp. 678-695, 2016.
[2]
Zhang H., Wen J., Cui J., and Zhang S., Efficient conditional privacy-preserving and authentication scheme for secure service provision in vanet, Tsinghua Science and Technology, vol. 21, no. 6, pp. 620-629, 2016.
[3]
Triboan D., Chen L., Chen F., and Wang Z., Towards a service-oriented architecture for a mobile assistive system with real-time environmental sensing, Tsinghua Science and Technology, vol. 21, no. 6, pp. 581-597, 2016.
[4]
Fischer A., Diaz M., Plamondon R., and Ferrer M. A., Robust score normalization for dtw-based on-line signature verification, in Proc. 13th Int. Conf. on Document Analysis and Recognition (ICDAR), Tunis, Tunisa, 2015, pp. 241-245.
[5]
Guerbai Y., Chibani Y., and Hadjadji B., The effective use of the one-class SVM classifier for handwritten signature verification based on writer-independent parameters, Pattern Recognition, vol. 48, no. 1, pp. 103-113, 2015.
[6]
Liu N., Zhang M., Li H., Sun Z., and Tan T., Deepiris: Learning pairwise filter bank for heterogeneous iris verification, Pattern Recognition Letters, vol. 82, no. 2, pp. 154-161, 2015.
[7]
Wang X., Zhang Y., and Yang H., A bimodal biometric verification system based on fingerprint and face, in Proc. 5th Int. Conf. Electronics Information and Emergency Communication, Beijing, China, 2015, pp. 325-327.
[8]
Zhang Y., Sankaranarayanan S., and Somenzi F., Statistically sound verification and optimization for complex systems, in Automated Technology for Verification and Analysis, Cham, Germany, 2014, pp. 411-427.
[9]
Hinton G., Deng L., Yu D., Dahl G. E., Mohamed A. R., Jaitly N., Senior A., Vanhoucke V., Nguyen P., Sainath T. N., et al., Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups, Signal Processing Magazine, vol. 29, no. 6, pp. 82-97, 2012.
[10]
Winograd S., On computing the discrete fourier transform, Mathematics of Computation, vol. 32, no. 141, pp. 175-199, 1978.
[11]
Flanagan J. L., Speech Analysis Synthesis and Perception. Berlin, Germany: Springer Science & Business Media, 2013.
[12]
Kamath S. and Loizou P., A multi-band spectral subtraction method for enhancing speech corrupted by colored noise, in IEEE international Conference on Acoustics Speech and Signal Processing, Orlando, FL, USA, 2002, p. 4164.
[13]
Schuster A., On the investigation of hidden periodicities with application to a supposed 26 day period of meteorological phenomena, Terrestrial Magnetism, vol. 3, no. 1, pp. 13-41, 1898.
[14]
Yang X. S., Flower pollination algorithm for global optimization, in Unconventional Computation and Natural Computation, Berlin, Germany, 2012, pp. 240-249.
[15]
McCulloch W. S. and Pitts W., A logical calculus of the ideas immanent in nervous activity, The Bulletin of Mathematical Biophysics, vol. 5, no. 4, pp. 115-133, 1943.
[16]
Leshno M., Lin V. Y., Pinkus A., and Schocken S., Multilayer feedforward networks with a nonpolynomial activation function can approximate any function, Neural Networks, vol. 6, no. 6, pp. 861-867, 1993.
[17]
Sibi P., Jones S. A., and Siddarth P., Analysis of different activation functions using back propagation neural networks, Journal of Theoretical and Applied Information Technology, vol. 47, no. 3, pp. 1264-1268, 2013.
[18]
Møller M. F., A scaled conjugate gradient algorithm for fast supervised learning, Neural Networks, vol. 6, no. 4, pp. 525-533, 1993.
[19]
Wirow B., Greenblatt A., Kim Y., and Park D., The no-prop algorithm: A new learning algorithm for multilayer neural networks, Neural Networks, vol. 37, pp. 182-188, 2013.
[20]
Werbos P., Beyond regression: New tools for prediction and analysis in the behavioral sciences, PhD dissertation, Havard University, Cambridge, MA, USA, 1974.
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Publication history

Received: 30 March 2017
Revised: 21 June 2017
Accepted: 29 June 2017
Published: 08 November 2018
Issue date: February 2019

Copyright

© The author(s) 2019

Acknowledgements

Authors acknowledge the contribution to this project from the Diamond Grant 2016 (No. 0080/DIA/2016/45) funded by the Polish Ministry of Science and Higher Education.

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