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.
This is a preview of subscription content, log in to check access.
Buy single article
Instant access to the full article PDF.
Price includes VAT for USA
Zhdanov, A.A., Avtonomnyi iskusstvennyi intellekt (Autonomous Artificial Intelligence), Moscow: Binom. Laboratoriya znanii, 2008.
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. 142–157
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.
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. 550–554
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. 833–838
Nussbaumer, H.J., Fast Fourier Transform and Convolution Algorithms, Springer, 1982.
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.
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.
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.
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.
Campbell, W.M., et al., Support vector machines for speaker and language recognition, Comput. Speech Lang., 2006, vol. 20, pp. 210–229.
Haykin, S., Neural Networks: A Comprehensive Foundation, Prentice Hall, 1998, 2nd ed.
Original Russian Text © N.N. Diep, A.A. Zhdanov, 2018, published in Programmirovanie, 2018, Vol. 44, No. 3.
About this article
Cite this article
Diep, N.N., Zhdanov, A.A. Neuron-Like Approach to Speech Recognition. Program Comput Soft 44, 170–180 (2018). https://doi.org/10.1134/S0361768818030088