Izvestiya of Saratov University.

Mathematics. Mechanics. Informatics

ISSN 1816-9791 (Print)
ISSN 2541-9005 (Online)


For citation:

Savin A. N., Timofeeva N. E., Geraskin A. S., Mavlutova Y. A. Development of Speech Recognition Systems Based on Hidden Markov Models of Individual Words. Izvestiya of Saratov University. Mathematics. Mechanics. Informatics, 2017, vol. 17, iss. 4, pp. 452-464. DOI: 10.18500/1816-9791-2017-17-4-452-464, EDN: ZXJPON

This is an open access article distributed under the terms of Creative Commons Attribution 4.0 International License (CC-BY 4.0).
Published online: 
28.11.2017
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Russian
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UDC: 
004.934
EDN: 
ZXJPON

Development of Speech Recognition Systems Based on Hidden Markov Models of Individual Words

Autors: 
Savin Aleksandr Nikolaevich, Saratov State University
Timofeeva Nadezhda Evgen'evna, Saratov State University
Geraskin Aleksej Sergeevich, Saratov State University
Mavlutova Yuliya Albertovna, Saratov State University
Abstract: 

The results of the development of software modules implementing the speech recognition system based on the hidden Markov models of individual words and the use of linear prediction in the coding of signs of an audio signal are presented. The structure of the speech recognition system is based on the hidden Markov models of individual words, consisting of four modules: a module for extracting words from the sound stream, a module for analyzing the features of a word, a module for learning the hidden Markov models, and a word recognition module. Algorithms for the formation of hidden Markov models with left-right topology for individual words of the required dictionary of commands of the object control system are based on the coding of signs of a sound signal using linear predictions. Results of an estimation of reliability of a sequence of observations corresponding to separate words obtained with the help of the proposed processing algorithm are given. The developed software modules allow to prepare efficiently  the necessary initial data and thus form the required dictionary of commands of the object management system, build hidden Markov models of individual words, and conduct their training using the Baum - Welsh algorithm. The designed command dictionaries are supposed to be used in intelligent control systems for various objects.

References: 
  1. Zhilyakov E. G., Babarinov S. L., Chadyuk P. V. Google Inc. Russian Speech Recognition Service Research. Belgorod State University Scientific Bulletin. Ser. History. Political science. Economics. Information technologies, 2013, no. 15(158), iss. 27/1, pp. 247–255 (in Russian).
  2. Titov Y. N. Modern technologies of speech recognition. Tambov University Reports. Ser. Natural and Technical Sciences, 2006, vol. 11, iss. 4, pp. 571–574 (in Russian).
  3. Savin A. N., Timofeeva N. E., Geraskin A. S., Mavlutova Yu. A. The development of software components for streaming audio content filtering through the use of hidden Markov models. Izv. Saratov Univ. (N.S.), Ser. Math. Mech. Inform., 2015, vol. 15, iss. 3, pp. 340 350. DOI: https://doi.org/10.18500/1816-9791-2015-15-3-340-350.
  4. Rabiner L. R. A tutorial on hidden Markov models and selected applications in speech recognition. Proceedings of the IEEE, 1989, vol. 77, no. 2. pp. 257–286. DOI: https://doi.org/10.1109/5.18626.
  5. Portal of company National Instruments Russia. Available at: http://www.labview.ru (accessed 25 December, 2012).
  6. Blahut R. E. Fast Algorithms for Digital Signal Processing. Addison-Wesley Publ. Co; Repr. with corrections edition, 1987. 441 p. (Russ. ed. : Moscow, Мir, 1989. 448 p.).
  7. Adler Ju. P., Markova E. V., Granovskij Ju. V. Planirovanie eksperimenta pri poiskeoptimal’nykh uslovii [Planning an experiment searching for optimal conditions]. Moscow, Nauka, 1976. 279 p. (in Russian).
Received: 
08.07.2017
Accepted: 
09.11.2017
Published: 
05.12.2017
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