Abstract
The internal speech recognition is a promising technology, which could find its use in brain-computer interfaces development and greatly help those who suffer from neurodegenerative diseases. The research in this area is in its early stages and is associated with practical value, which makes it relevant. It is known that internal pronunciation can be restored according to electroencephalogram data because it allows one to register specific activity associated with this process. The purpose of this work is to build and implement an algorithm for extracting features and classifying Russian phonemes according to an electroencephalogram recorded during the internal pronunciation of the phonemes. This kind of research is actively conducted abroad; however, there is no information about such works for the Russian language phonemes in open sources at the moment. In the course of the work, an algorithm for extracting features and classifying the internal pronunciation of Russian phonemes was built and tested, the accuracy of which showed results comparable with other studies.
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The research is financially supported by the Russian Science Foundation, Project â„–20-18-00067.
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Gavrilenko, Y., Saada, D., Ilyushin, E., Vartanov, A.V., Shevchenko, A. (2021). The Electroencephalogram Based Classification of Internally Pronounced Phonemes. In: Samsonovich, A.V., Gudwin, R.R., Simões, A.d.S. (eds) Brain-Inspired Cognitive Architectures for Artificial Intelligence: BICA*AI 2020. BICA 2020. Advances in Intelligent Systems and Computing, vol 1310. Springer, Cham. https://doi.org/10.1007/978-3-030-65596-9_13
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