Multiagent model of perceptual space formation
News of the Kabardin-Balkar scientific center of RAS, no. 3 (2020), pp. 13-22.

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The model of the early development of language competencies proposed in this paper, takes into account the social factors effects. It is a simulation model of phonemic imprinting. The model describes the process of perceiving audio stimuli as their mapping into classes of elementary language units. The machine learning algorithm was developed using the results of the study of speech addressed to children. Our model will allow to explore the features of phonetic perception, the cognitive mechanisms that underlie language development, highlight the main factors affecting the duration of the plasticity period. The proposed model gives possibilities to build perceptual maps, design diagnostic tools to describe and study the sensitive period. The model can also be used to create speech systems that are resistant to various influences and effective when used in conditions of high noise.
Keywords: multiagent systems, artificial intelligence, artificial neuron nertworks, speech recognition, plasticity period, motherese.
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Z. V. Nagoev; I. A. Gurtueva; K. Ch. Bzhikhatlov. Multiagent model of perceptual space formation. News of the Kabardin-Balkar scientific center of RAS, no. 3 (2020), pp. 13-22. http://geodesic.mathdoc.fr/item/IZKAB_2020_3_a1/

[1] D. Jurafsky, J. Martin, Speech and Language Processing: An introduction to natural language processing, computational linguistics, and speech recognition, Prentice Hall, Boston, 2008, 1032 pp.

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

[3] Andreas Stolcke, Jasha Droppo, “Comparing Human and Machine Errors in Conversational Speech Transcription”, Interspeech 2017, 137-141 | DOI

[4] Saon George, Kurata Gakuto, Sercu Tom, Audhkhasi Kartik, Thomas Samuel, Dimitriadis Dimitrios, Cui Xiaodong, Ramabhadran Bhuvana, Picheny Michae, Lim Lynn-Li, Roomi Bergul, Hall Phil, “English Conversational Telephone Speech Recognition by Humans and Machines”, Interspeech 2017, 132–136 | DOI

[5] W. Strange, Speech Perception and Linguistic Experience: Issues in Cross-Language Research, York Press, Baltimore, Baltimore, 1995, 492 pp.

[6] S. N. Tseitlin, A Child and a Language: Child Speech Linguistics, Humanitarian Publishing Center VLADOS, Moscow, 2000

[7] N. A. Chomsky, “A Review of Skinner's Verbal Behavior”, Readings in the Psychology of Language, eds. L. A. Jakobovits, , M. S. Miron, Prentice-Hall, Boston, 1967, 636

[8] V. P. Morozov, I. A. Vartanyan, V. I. Galunov, Speech Perception: Problems of Functional Brain Asymmetry, Science, St. Petersburgh, 1988

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

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

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

[12] S. Pinker, The Language Instinct: How the Mind Creates Language, Harper Perennial, New-York, 2007, 494 pp.

[13] Iu Kotseruba, J. K. Tsotsos, A Review of 40 Years of Cognitive Architecture Research: Core Cognitive Abilities and Practical Applications, arXiv: abs/1610.08602

[14] M. Wooldridge, An Introduction to Multi-Agent Systems, Wiley, Hoboken, New-Jersey, 2009, 366 pp.

[15] Z. V. Nagoev, “Intellectics, or thinking in living and artificial systems”, Publishing House KBSC RAS, 2013, Nalchik

[16] 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

[17] 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

[18] Z. Nagoev, L. Lyutikova, I. Gurtueva, “Model for Automatic Speech Recognition Using Multi-Agent Recursive Cognitive Architecture”, Annual International Conference on Biologically Inspired Cognitive Architectures BICA (Prague, Czech Republic) | DOI

[19] Z. Nagoev, I. Gurtueva, D. Malyshev, Z. Sundukov, “Multi-agent Algorithm Imitating Formation of Phonemic Awareness”, Biologically Inspired Cognitive Architectures 2019. BICA 2019, Advances in Intelligent Systems and Computing, 948, ed. A. Samsonovich, Springer, Cham | DOI

[20] Z. V. Nagoev, I. A. Gurtueva, “Fundamental Elements for Cognitive Model of Speech Perception Mechanism Based on Multiagent Recursive Intellect”, News of Kabardino-Balkarian Scientific Center of RAS, 2019, no. 3 (89), 3–14

[21] O. Garnica, “Some prosodic and paralinguistic features of speech to young children”, Talking to Children, eds. C. Snow, Ch. Ferguson, Cambridge University Press, Cambridge, 1977, 63

[22] A. Fernald, “Four-month-old infants prefer to listen to motherese”, Infant Behavior and Development, 1985, no. 8, 181–195 | DOI

[23] A. Fernald, P. Kuhl, “Acoustic determinants of infant preference for Motherese Speech”, Infant Behavior and Development, 1987, no. 10, 279–93 | DOI

[24] E. L. Moerk, “Principles of interaction in language learning”, Merril-Palmer Quaterly, 1972, no. 18, 229–257

[25] Pye C. Quich', “Mayan speech to children”, Journal of Child Language, 13:1 (1986), 85–100 | DOI | MR

[26] Vygotsky L.S., Thinking and Speech, Piter, St-Petersburg, 2019

[27] B. T. Conboy, P. K. Kuhl, “Impact of second-language experience in infancy: Brain measures of first- and second-language speech perception”, Developmental Science, 2011, no. 14, 242–248 | DOI

[28] A. J. Doupe, P. K. Kuhl, “Birdsong and human speech: Common themes and mechanisms”, Neuroscience of birdsong, eds. H. P. Zeigler, P. Marler, Cambridge University Press, 2008, 5–31

[29] A. De Cheveigne, H. Kawahara, “YIN, a fundamental frequency estimator for speech and music”, The Journal of the Acoustical Society of America, 111:4 (2002), 1917–1930 | DOI

[30] R. C. Maher, J. W. Beauchamp, “Fundamental frequency estimation of musical signals using a two-way mismatch procedure”, The Journal of the Acoustical Society of America, 95:4 (1994), 2254 | DOI

[31] A. Coates, A. Y. Ng, Learning Feature Representations with K-Means, Lecture Notes in Computer Science, 7700, eds. G. Montavon, G. B. Orr, K. R. Müller, Springer, Berlin–Heidelberg, 2012

[32] L. R. Zinder, “The General Phonetics”, The Higher School, Moscow, 1979