Logo PTI
Polish Information Processing Society
Logo FedCSIS

Annals of Computer Science and Information Systems, Volume 8

Proceedings of the 2016 Federated Conference on Computer Science and Information Systems

Neuro-heuristic voice recognition

DOI: http://dx.doi.org/10.15439/2016F128

Citation: Proceedings of the 2016 Federated Conference on Computer Science and Information Systems, M. Ganzha, L. Maciaszek, M. Paprzycki (eds). ACSIS, Vol. 8, pages 487–490 ()

Full text

Abstract. Protection of private data, signing electronic documents are selective use of identity verification. In this work, the problem of voice verification has been discussed. The method of verifying the voice based on the methods of artificial intelligence was presented. Numerous tests were performed to demonstrate the effectiveness of the presented solution - research results are shown and discussed in terms of advantages and disadvantages.

References

  1. D. Połap and M. Woźniak, “Flexible neural network architecture for handwritten signatures recognition,” International Journal of Electronics and Telecommunications, vol. 62, no. 2, pp. 197–202, 2016, http://dx.doi.org/10.1515/eletel-2016-0027.
  2. G. Capizzi, G. Lo Sciuto, C. Napoli, E. Tramontana, and M. Woźniak, “Automatic classification of the fruit defects based on co-occurrence matrix and neural networks,” in Proceedings of the Federated Conference on Computer Science and Information Systems - FedCSIS’2015. 13-16 September, Lodz, Poland: IEEE, 2015, pp. 861–867, http://dx.doi.org/10.15439/2015F258.
  3. M. Knop, T. Kapuscinski, W. K. Mleczko, and R. A. Angryk, “Neural video compression based on RBM scene change detection algorithm,” Lecture Notes in Artificial Intelligence - ICAISC’2016, vol. 9693, pp. 660–669, 2016, http://dx.doi.org/10.1007/978-3-319-39384-1 58.
  4. R. Damaševic̆ius, M. Vasiljevas, I. Martišius, V. Jusas, D. Birvinskas, A. Venc̆kauskas, and M. Woźniak, “BoostEMD: an extension of EMD method and its application for denoising of EMG signals,” Elektronika IR Elektrotechnika, vol. 21, no. 6, pp. 57–61, 2015, http://dx.doi.org/10.5755/j01.eee.21.6.13763.
  5. I. Martisius, D. Birvinskas, R. Damasevicius, and V. Jusas, “EEG dataset reduction and classification using wave atom transform,” in Proceedings of the 23rd International Conference on Artificial Neural Networks and Machine Learning; ICANN 2013 - Volume 8131. New York, NY, USA: Springer-Verlag New York, Inc., 2013, pp. 208–215, http://dx.doi.org/10.1007/978-3-642-40728-4_26.
  6. K. Cpałka, O. Rebrova, R. Nowicki, and L. Rutkowski, “On design of flexible neuro-fuzzy systems for nonlinear modelling,” International Journal of General Systems, vol. 42, no. 6, pp. 706–720, 2013.
  7. C. Napoli, G. Pappalardo, E. Tramontana, R. K. Nowicki, J. T. Starczewski, and M. Woźniak, “Toward automatic work groups classification based on probabilistic neural network approach,” Lecture Notes in Artificial Intelligence - ICAISC’2015, vol. 9119, pp. 79–89, 2015, http://dx.doi.org/10.1007/978-3-319-19324-3_8.
  8. R. Damaševic̆ius, M. Vasiljevas, J. Salkevicius, and M. Woźniak, “Human activity recognition in aal environments using random projections,” Computational and Mathematical Methods in Medicine, vol. 2016, pp. 4 073 584:1–4 073 584:17, 2016, http://dx.doi.org/10.1155/2016/4073584.
  9. M. Woźniak, D. Połap, R. K. Nowicki, C. Napoli, G. Pappalardo, and E. Tramontana, “Novel approach toward medical signals classifier,” in IEEE IJCNN 2015 - 2015 IEEE International Joint Conference on Neural Networks, Proceedings. 12-17 July, Killarney, Ireland: IEEE, 2015, pp. 1924–1930, http://dx.doi.org/10.1109/IJCNN.2015.7280556.
  10. Y. Zhang, S. Sankaranarayanan, and F. Somenzi, “Statistically sound verification and optimization for complex systems,” in Automated Technology for Verification and Analysis. Springer, 2014, pp. 411–427.
  11. G. Hinton, L. Deng, D. Yu, G. E. Dahl, A.-r. Mohamed, N. Jaitly, A. Senior, V. Vanhoucke, P. Nguyen, T. N. Sainath et al., “Deep neural networks for acoustic modeling in speech recognition: The shared views of four research groups,” Signal Processing Magazine, IEEE, vol. 29, no. 6, pp. 82–97, 2012.
  12. S. Winograd, “On computing the discrete fourier transform,” Mathematics of computation, vol. 32, no. 141, pp. 175–199, 1978.
  13. J. L. Flanagan, Speech analysis synthesis and perception. Springer Science & Business Media, 2013, vol. 3.
  14. S. Kamath and P. Loizou, “A multi-band spectral subtraction method for enhancing speech corrupted by colored noise,” in IEEE international conference on acoustics speech and signal processing, vol. 4. Citeseer, 2002, pp. 4164–4164.
  15. A. Schuster, “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.
  16. X.-S. Yang, “Flower pollination algorithm for global optimization,” in Unconventional computation and natural computation. Springer, 2012, pp. 240–249.
  17. W. S. McCulloch and W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, vol. 5, no. 4, pp. 115–133, 1943.
  18. P. Sibi, S. A. Jones, and P. Siddarth, “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.
  19. P. Werbos, “Beyond regression: New tools for prediction and analysis in the behavioral sciences,” 1974.