Geodesic
Cognitive model for speech perception mechanism
News of the Kabardin-Balkar scientific center of RAS, no. 6-3 (2018), pp. 24-33.

Voir la notice de l'article provenant de la source Math-Net.Ru

This paper proposes the formal description of the structure of an act of speech perception, which is necessary as a general theoretical basis for the development of universal automatic speech recognition systems of high performance in real operation conditions and cocktail party situations. The general structural dynamics of the speech recognition process has been developed. The necessity of using the articulation event as a minimal basic pattern of sound image recognition has been proved. Multi-agent systems were chosen as the formal means of implementation.
Keywords: speech recognition, speech perception, cognitive architectures, multiagent systems, artificial intellect. 
@article{IZKAB_2018_6-3_a1,
     author = {Z. V. Nagoev and I. A. Gurtueva},
     title = {Cognitive model for speech perception mechanism},
     journal = {News of the Kabardin-Balkar scientific center of RAS},
     pages = {24--33},
     publisher = {mathdoc},
     number = {6-3},
     year = {2018},
     language = {ru},
     url = {http://geodesic.mathdoc.fr/item/IZKAB_2018_6-3_a1/}
}
TY  - JOUR
AU  - Z. V. Nagoev
AU  - I. A. Gurtueva
TI  - Cognitive model for speech perception mechanism
JO  - News of the Kabardin-Balkar scientific center of RAS
PY  - 2018
SP  - 24
EP  - 33
IS  - 6-3
PB  - mathdoc
UR  - http://geodesic.mathdoc.fr/item/IZKAB_2018_6-3_a1/
LA  - ru
ID  - IZKAB_2018_6-3_a1
ER  - 
%0 Journal Article
%A Z. V. Nagoev
%A I. A. Gurtueva
%T Cognitive model for speech perception mechanism
%J News of the Kabardin-Balkar scientific center of RAS
%D 2018
%P 24-33
%N 6-3
%I mathdoc
%U http://geodesic.mathdoc.fr/item/IZKAB_2018_6-3_a1/
%G ru
%F IZKAB_2018_6-3_a1
Z. V. Nagoev; I. A. Gurtueva. Cognitive model for speech perception mechanism. News of the Kabardin-Balkar scientific center of RAS, no. 6-3 (2018), pp. 24-33. http://geodesic.mathdoc.fr/item/IZKAB_2018_6-3_a1/

[1] D. Jurafsky, J. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition, 2nd edition, Prentice Hall, 2008, 1024 pp.

[2] A. Marti, M. Cobos, J. Lopez, “Automatic Speech Recognition in Cocktail-Party Situations: A specific Training for Separated Speech”, The Journal of the Acoustical Society of America, 2012, 1529–1535 | DOI

[3] E. M. Zion Golumbic, N. Ding, S.et al. Bickel, “Mechanisms underlying selective neuronal tracking of attended speech at a “cocktail party””, Neuron, 77:5 (2013), 980–991 | DOI

[4] I. L. Mazurenko, “Kompyuternye sistemy raspoznavaniya rechi”, Intellektualnye sistemy, 3:1–2 (1998), 117–134

[5] Z. V. Nagoev, Intellektika ili Myshlenie v zhivykh i iskusstvennykh sistemakh, Izdatelstvo KBNTs RAN, Nalchik, 2013, 211 pp.

[6] V. Gupta, “A Survey of Natural Language Processing Techniques”, International Journal of Computer Science Engineering Technology (IJCSET), 5:1 (2014), 14–16

[7] W. Ghai, N. Singh, “Literature Review on Automatic Speech Recognition”, International Journal of Computer Applications, 41:8 (2012), 42–50 | DOI

[8] R. Reddy, “Speech Recognition by Machine: A Review”, Proceedings of the IEEE, 64:4 (1976), 501–531 | DOI

[9] B. P. Tazhev, I. A. Gurtueva, “O nekotorykh podkhodakh k resheniyu zadachi avtomaticheskogo raspoznavaniya rechi”, Trudy mezhdunarodnoi konferentsii po kompyuternoi i kognitivnoi lingvistike (TEL 2016) (Kazan, 21-24 aprelya 2016 g.), 217–220

[10] A. Waibel, K. F. Lee, Readings in Speech Recognition, Morgan Kaufman, 1990, 680 pp.

[11] J. F. Hemdal, G. W. Hughes, “A Feature Based Computer Recognition Program for the Modeling of Vowel Perception”, Models for the Perception of Speech and Visual Form, eds. W. Wathen-Dunn, MIT Press, Cambridge, MA, 1967

[12] N. Pappas, T. Meyera, A Survey on Language Modeling Using Neural Networks, Idiap-Research Report-32-2012, 2012, 21 pp.

[13] A. I. Galushkin, “O metodakh nastroiki mnogosloinykh neironnykh setei”, XII Vserossiiskoe soveschanie po problemam upravleniya VSPU (Moskva, 16-19 iyunya 2014 g.), 2014, 3936–3947

[14] W. De Mulder, S. Bethard, M. F. Moens, “A Survey on the Application of Recurrent Neural Networks to Statistical Language Modeling”, Computer Speech and Language, 30:1 (2015), 61–98 | DOI

[15] L. Deng, X. Li, “Machine Learning Paradigms for Speech Recognition: An Overview”, IEEE Transactions on Audio, Speech, and Language Processing, 21:5 (2013), 1060–1089 | DOI | MR

[16] A. Mohamed, G. Dahl, G. Hinton, “Acoustic modeling using deep belief networks”, IEEE Audio, Speech, Lang. Process, 20:1 (2012), 14–22 | DOI

[17] G. Hinton, L. Deng, D. Yu, al. et, “Deep neural networks for acoustic modeling in speech recognition”, IEEE Signal Process. Mag, 29:6 (2012), 82–97 | DOI | MR

[18] L. Deng, X. Li, “Machine Learning Paradigms for Speech Recognition: An Overview”, IEEE Transactions on Audio, Speech, and Language Processing, 21:21 (2013), 1060–1089 | DOI | MR

[19] O. Abdel-Hamid, A. Mohamed, H. Jiang, G. Penn, “Applying convolutional neural networks concepts to hybrid NN-HMM model for speech recognition”, Proc. IEEE Int. Conf. Acoust., Speech, Signal Process, 2012, 4277–4280

[20] L. R. Zinder, Obschaya fonetika, Vyssh. shkola, M., 1979, 315 pp.

[21] M. Minsky, The Society of Mind, Simon and Shuster, New York, 1988, 336 pp.

[22] M. S. Ed. Gazzaniga, Conversations in the Cognitive Neuroscience, The MIT Press, 1999, 202 pp.

[23] N. A. Chomsky, “A Review of Skinner's Verbal Behavior”, Readings in the Psychology of Language, eds. L.A. Jakobovits and M.S. Miron, 1967, 142

[24] P. Haikonen, The Cognitive Approach to Conscious Machines, imprint Academic, Exeter, UK, 2003, 300 pp.

[25] A. Newell, Unified Theories of Cognition, Harvard University Press, Cambridge, Massachusetts, 1990, 576 pp.

[26] F. De Sossyur, Kurs obschei lingvistiki, eds. Sh. Balli, A. Seshe, Izd-vo Ural. un-ta, Ekaterinburg, 1999, 432 pp.

[27] D. H. Schunk, Learning Theories: An Educational Perspective, 6th, Pearson Merrill Prentice Hall, 2011, 576 pp.

[28] M. Wooldridge, An Introduction to MultiAgent Systems, Wiley, 2009, 484 pp.

[29] Setevaya biblioteka otkrytogo dostupa, arXiv: (data obrascheniya: 01.10.2017) abs/1610.08602

[30] Z. V. Nagoev, O. V. Nagoeva, “Zritelnyi analizator intellektualnogo robota dlya obrabotki nestrukturirovannykh dannykh na osnove multiagentnoi neirokognitivnoi arkhitektury”, Perspektivnye sistemy i zadachi upravleniya, Materialy XII Vserossiiskoi nauchno-prakticheskoi konferentsii, YuFU, Rostov-na-Donu, 2017, 457–467

[31] V. P. Morozov, I. A. Vartanyan, V. I. Galunov, Vospriyatie rechi: voprosy funktsionalnoi asimmetrii mozga, Nauka, L., 1988, 135 pp.

[32] B. D. Van Veen, K. M. Buckley, “Beamforming: A Versatile Approach to Spatial Filtering”, IEEE ASSP Magazine, 5:2 (1988), 4–24 | DOI

[33] Z. V. Nagoev, O. V. Nagoeva, “Izvlechenie znanii iz mnogomodalnykh potokov nestrukturirovannykh dannykh na osnove samoorganizatsii multiagentnoi kognitivnoi arkhitektury mobilnogo robota”, Izvestiya KBNTs RAN, 2015, no. 6 (68), 73–85

[34] Z. V. Nagoev, V. A. Denisenko, L. A. Lyutikova, “Sistema obucheniya avtonomnogo selskokhozyai-stvennogo robota raspoznavaniyu staticheskikh izobrazhenii na osnove multiagentnykh kognitivnykh arkhitektur”, Ustoichivoe razvitie gornykh territorii, 2018, no. 2, 289–297