Programming and Computer Software

, Volume 44, Issue 3, pp 170–180 | Cite as

Neuron-Like Approach to Speech Recognition

  • N. N. DiepEmail author
  • A. A. Zhdanov


In this paper, we present a new approach to speech recognition based on A. Zhdanov’s biomorphic neuron-like networks, which is also known as the autonomous adaptive control (AAC) method. In contrast to artificial neural networks (ANNs), a neuron in the AAC method is itself a self-learning pattern recognition system. We attempt to build a speech recognition system as a construction of such neurons without a program component. If this attempt is successful, then we will be able to simulate the natural principle of speech recognition not only in a program way but also via parallel hardware implementations. We understand the speech recognition problem as one of the speech processes in natural nervous systems that is to be simulated.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Zhdanov, A.A., Avtonomnyi iskusstvennyi intellekt (Autonomous Artificial Intelligence), Moscow: Binom. Laboratoriya znanii, 2008.Google Scholar
  2. 2.
    Zhdanov, A.A., On the concept of autonomous artificial intelligence, in Iskusstvennyi intellekt v tekhnicheskikh sistemakh (Artificial Intelligence in Technical Systems), Moscow: Gos. Inst. Fiz. Tekh. Probl., 1997, pp. 142–157Google Scholar
  3. 3.
    Zhdanov, A.A. and Naumkina, T.S., Modeling of language phenomena in neuron-like control systems, Sb. nauchn. tr. Vserossiiskoi nauchno-tekhnicheskoi konferentsii Neiroinformatika-2007 (Proc. All-Russ. Sci.-Tech. Conf. Neuroinformatics-2007), part 3, pp. 76–84.Google Scholar
  4. 4.
    Zhdanov, A., Kondukov, A., Naumkina, T., and Dmitrienko, O., Automatic origin of a language in AAC neuron-like systems, Proc. 11th Int. Conf. Speech and Computer (SPECOM), 2006, pp. 550–554Google Scholar
  5. 5.
    Zhdanov, A.A. and Naumkina, T.S., Modeling of formation of extralinguistic factors influencing the attitude of the recipient towards language messages, Proc. 12th Int. Conf. Speech and Computer (SPECOM), 2007, pp. 833–838Google Scholar
  6. 6.
    Nussbaumer, H.J., Fast Fourier Transform and Convolution Algorithms, Springer, 1982.CrossRefzbMATHGoogle Scholar
  7. 7.
    Neiman, L.V., Bogomil’skii, M.R., and Seliverstov, V.I., Anatomiya, fiziologiya i patologiya organov slukha i rechi (Anatomy, Physiology, and Pathology of the Organs of Hearing and Speech), Moscow: Vlados, 2001.Google Scholar
  8. 8.
    Lindasalwa, M., Begam, M., and Elamvazuthi, I., Voice recognition algorithms using Mel frequency cepstral coefficient (MFCC) and dynamic time warping (DTW) techniques, 2010, Preprint no. 1003.4083.Google Scholar
  9. 9.
    Zhdanov, A.A., Kryzhanovskii, M.V., and Preobrazhenskii, N.B., Bionic adaptive intelligent control system for a mobile robot, Iskusstvennyi Intellekt, 2002, vol. 4, pp. 341–350.Google Scholar
  10. 10.
    Gefke, D.A. and Zatsepin, P.M., Application of hidden Markov models to recognition of sound sequences, Izv. Altaisk. Gos. Univ., 2012, nos. 1–2, pp. 72–76.Google Scholar
  11. 11.
    Campbell, W.M., et al., Support vector machines for speaker and language recognition, Comput. Speech Lang., 2006, vol. 20, pp. 210–229.CrossRefGoogle Scholar
  12. 12.
    Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998, 2nd ed.zbMATHGoogle Scholar

Copyright information

© Pleiades Publishing, Ltd. 2018

Authors and Affiliations

  1. 1.Moscow Institute of Physics and TechnologyDolgoprudnyi, Moscow oblastRussia
  2. 2.Lebedev Institute of Precision Mechanics and Computer EngineeringRussian Academy of SciencesMoscowRussia

Personalised recommendations