Determination of input parameters of the neural network model, intended for phoneme recognition of a voice signal in the systems of distance learning

Authors

  • Berik Akhmetov Caspian State University of Technologies and Engineering named after Sh. Yessenov
  • Igor Tereykovsky National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"
  • Aliya Doszhanova Almaty University of Power Engineering and Telecommunications
  • Lyudmila Tereykovskaya Kyiv National University of Construction Architecture

Abstract

The article is devoted to the problem of voice
signals recognition means introduction in the system of distance
learning. The results of the conducted research determine the
prospects of neural network means of phoneme recognition.
It is also shown that the main diculties of creation of the
neural network model, intended for recognition of phonemes
in the system of distance learning, are connected with the
uncertain duration of a phoneme-like element. Due to this
reason for recognition of phonemes, it is impossible to use
the most eective type of neural network model on the basis
of a multilayered perceptron, at which the number of input
parameters is a xed value. To mitigate this shortcoming, the
procedure, allowing to transform the non-stationary digitized
voice signal to the xed quantity of mel-cepstral coecients,
which are the basis for calculation of input parameters of
the neural network model, is developed. In contrast to the
known ones, the possibility of linear scaling of phoneme-
like elements is available in the procedure. The number of
computer experiments conrmed expediency of the fact that
the use of the oered coding procedure of input parameters
provides the acceptable accuracy of neural network recognition
of phonemes under near-natural conditions of the distance
learning system. Moreover, the prospects of further research in
the eld of development of neural network means of phoneme
recognition of a voice signal in the system of distance learning
is connected with an increase in admissible noise level. Besides,
the adaptation of the oered procedure to various natural
languages, as well as to other applied tasks, for instance, a
problem of biometric authentication in the banking sector, is
also of great interest.

Author Biographies

Berik Akhmetov, Caspian State University of Technologies and Engineering named after Sh. Yessenov

Rector

Igor Tereykovsky, National Technical University of Ukraine "Igor Sikorsky Kyiv Polytechnic Institute"

professor

Aliya Doszhanova, Almaty University of Power Engineering and Telecommunications

assosiated professor of the Department IT-engineering

References

V. Mikhaylenko Neural network models and methods of recognition

of phonemes in a voice signal in the system of distance learning:

[Monograph] / V. M. Mikhailenko, L. O. Tereykovskaya, I.

A. Tereykovsky., B. B. Akhmetov. - K .: CP "Komprint", 2017.-

p.

A Najib, A Basari, A Pee, M Daimon, A Rahman, L Tahir

ONLINE PERFORMANCE DIALOGUE SYSTEM MODEL

(e-DP): A REQUIREMENT ANALYSIS STUDY AT BATU

PAHAT DISTRICT EDUCATION OFFICE Journal of Theoretical

and Applied Information Technology. 31st December

Vol.95. No 24 P. 6699 6706.

A. Mosa, M. Mahrin, R. Yuso A SYSTEMATIC LITERATURE

REVIEW OF TECHNOLOGICAL FACTORS FOR ELEARNING

READINESS IN HIGHER EDUCATION. Journal

of Theoretical and Applied Information Technology. 30th

November 2016. Vol.93. No.2. P. 500 521.

I. Veritawati, I. Wasito, T. Basaruddin TEXT INTERPRETATION

USING A MODIFIED PROCESS OF THE ONTOLOGY

AND SPARSE CLUSTERING. Journal of Theoretical

and Applied Information Technology 15th March 2017. Vol.95.

No 5. P. 1019-1028.

A.Kadir, A. Yauri AUTOMATED SEMANTIC QUERY FORMULATION

USING MACHINE LEARNING APPROACH.

Journal of Theoretical and Applied Information Technology.

th June 2017. Vol.95. No 12. P. 2761-2775.

J. Park, J. Yoon, Y. Seo, G. Jang SPECTRAL ENERGY

BASED VOICE ACTIVITY DETECTION FOR REAL-TIME

VOICE INTERFACE. Journal of Theoretical and Applied

Information Technology. 15th September 2017. Vol. 95 No17.

P. 4304-4312.

A Agranovsky, D. Lednov Theoretical aspects of algorithms

for processing and classifying speech signals. - M .: Radio and

Communication, 2004. - Ch. 1. 164 c.

L. Babenko, D. Subbotin, V. Fedorov, P. Yurkov DEFINITION

OF THE BORDERS BETWEEN THE FONEMAS BY A

NEUROET NETWORK METHOD. Izvestiya Southern Federal

University. Technical science. 2003 4 Òîì33. Pp. 321-323.

T. Kartbayev, B. Akhmetov, A. Doszhanova, K. Mukapil,

A. Kalizhanova, G. Nabiyeva, L. Balgabayeva, F. Malikova

DEVELOPMENT OF A COMPUTER SYSTEM FOR IDENTITY

AUTHENTICATION USING ARTIFICIAL NEURAL

NETWORKS. Image Analysis & Stereology, 10.5566/ias.1612.

V.36, 1, 2017.

O. Fedyaev, I. Bondarenko Neural network algorithm for

speaker-independent recognition of speech phonemes. USIM,

, No. 4 C. 41- 50.

B. Meyer , T. Jurgens, T. Wesker, T. Brand, B. Kollmeier

Human phoneme recognition depending on speech-intrinsic

variability. J Acoust Soc Am. 2010 Nov;128(5):3126-41.

Y. Qian, M. Bi, T. Tan, K. Yu, "Very deep convolutional neural

networks for noise robust speech recognition," in IEEE/ACM

Trans. Audio Speech Language Process. , vol. 24, no. 12, pp.

-2276, 2016.

V. Lila, E. Puchkov Methodology of training a recurrent arti-

cial neural network with dynamic stack memory. International

magazine "Software products and systems", Tver, 4, 2014 p.

[on pages 132-135].

Understanding LSTM Networks Posted on August 27,

(http://colah.github.io/posts/2015-08-Understanding-

LSTMs/) .

A. Waibel, T. Hanazawa, G. Hinton, K. Shikano, K. Lang

¾Phoneme Recognition Using Time - Delay Neural Networks¿,

IEEE Transactions On Accoustics, Speech And Signal Processing,

Vol. 37, 1989.

M. Gusev Methods and models of recognition of Russian speech

in information systems: dis. ... doctors of techn. Sciences:

13.01 / MN Gusev - St. Petersburg, 2014. - 378 p.

I. Tereykovskii Optimization of the structure of a two-chirped

perceptron, possible distribution of fertility of anomalous inuences

of experimental parameters of computer technology / IA

Tereykovskii // Scientic and technical journal "Management

of branching of folded systems" Kiev. National University of

Architecture. - 2011. - Vol. 5. - S. 128-131.

I. Boykov, A. Ivanov, D. Kalashnikov ALGORITHM OF

THE CONSTRUCTION OF THE STATISTIC DISCRETECONTINUOUS

DESCRIPTION OF THE DURATION OF

THE AUDIO SOURCES OF THE INCREASED SPEECH OF

THE DICTOR. News of higher educational institutions. The

Volga region. 4 (36), 2015 p.64-76

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Published

2018-10-28

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